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Cognitive Effect Indicators: The Impact of Student and Teacher Styles on Course Grades

Edward W. McCann, Jr.

Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University

in partial fulfillment of the requirements for the degree of

Master of Science In

Career and Technical Education

Committee Members

Dr. Thomas Broyles Dr. Rick Rudd

Dr. William Price

April 21, 2008 Blacksburg, Virginia

Keywords: Cognitive effect, Problem solving style, Learning style, Associate’s degree, Course grades

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Cognitive Effect Indicators: The Impact of Student and Teacher Styles on Course Grades

By Edward W. McCann, Jr.

Abstract

This study was descriptive, correlative and explanatory. It summarized the problem

solving and learning styles of students enrolled in the spring 2008 Virginia Polytechnic Institute

and State University Agricultural Technology program, identified relationships between problem

solving and learning styles, and used problem solving and learning styles to explain students’

end of course grades. Ninety-three students and six faculty members elected to participate in the

study.

There were differences between degree options in terms of orientation to change. Second

year students were likely to be internal processors. There were not significant differences among

the population in ways of deciding. However, Agricultural Technology teachers were more task

oriented problem solvers, while their students were people oriented. Teachers were more field

independent than the students. There were no relationships between problem solving and

learning style. There was a high degree of association between student ways of deciding and

manner of processing.

Student and teacher problem solving and learning styles were used to explain 11% of the

variance in students’ end of course grades for the six teachers in the study. Student orientation to

change, student manner of processing, teacher manner of processing and teacher ways of

deciding scores produced a model that significantly explained end of course grades. Suggestions

for further research included identifying other career areas with stylistic trends and further

identifying the impact cognitive effect has on student behavior.

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Dedication

This paper is dedicated to my best friend Katie.

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Acknowledgements

I am grateful for the outpouring of assistance, encouragement, advice, and understanding

from many individuals while completing this study. I would like to begin by expressing my

sincere thanks to my advisor and friend, Tom Broyles. I respect the many long hours, insistent

dedication to students, and passion that he has for agricultural education. I hope that one day I

can become a teacher like he has been to me. I also appreciate the assistance, encouragement and

suggestions from the other members of my committee, Rick Rudd and Bill Price.

I would like to extend a warm thank you to the Virginia Polytechnic Institute and State

University Agricultural Technology program students, staff and faculty for participating in this

study. I would like to thank Stephanie White, program director, for her assistance, advice and

encouragement over my tenure of involvement in the program. It has been a pleasure working

with you. I am grateful for the sincere interest in my work, witty jokes and support from Sam

Doak. You always kept me on my toes. I am thankful for Pavli Mykerezi, who has consistently

been a reminder on why it is important to maintain a positive outlook on life, and the graduate

school process in general. I appreciate and admire the passion that Rachel Hensley has for her

students and for agriculture. I would like to thank Tom Martin for reminding me why it is

important to stand up for what you believe in. This study would not have been possible without

the help of Joe Guthrie. Thank you for your consistent humble attitude towards technology,

flexibility in the classroom, allowing me to participate in the class activities, and your

willingness to learn about and apply the theories covered in this project. I am grateful for the

innocent support and student perspective of my agricultural technology student services cohort,

Mark Tavares. Your determined attitude is very admirable. I also appreciate the technical

assistance, laughter and encouragement from the College of Agriculture and Life Sciences

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Academic Programs staff. Lastly, I am indebted to Brenda French for her wise Christian advice,

outpouring of support and her ability to always “make it happen”.

Great appreciation is also in order for my network of support within the Agricultural and

Extension Education Department. Many of the faculty have always had an open door policy for

my quick questions as well as the long and drawn out questions. I could not have made it through

this process without the other graduate students in the department. I thank you all for taking

“team-man” in stride and bringing new rigor and excitement to the graduate program.

I must extend a warm and gracious thank you towards my supportive family. My parents,

Mr. and Mrs. Edward W. McCann, have helped to instill in me a passion for learning, a tenacious

work ethic, and a zest for service to mankind. For without the countless prayers, phone calls and

occasional baked goods, this task would have been much more difficult.

I conclude this section by expressing my indebtedness to my soon to be wife, Ms. Katie

Rebecca Dews. I must agree with the verse 21 in chapter 18 of the book of Proverbs, “He who

finds a wife, finds what is good and receives favor from the LORD”. I would not have made it

through this process without your sincere understanding, listening ear, and kindhearted

friendship.

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Table of Contents

Abstract ....................................................................................................................................... ii

Dedication .................................................................................................................................. iii

Acknowledgements .................................................................................................................... iv

List of Tables ............................................................................................................................... x

List of Figures ............................................................................................................................ xi

Chapter 1 ..................................................................................................................................... 1

Introduction ................................................................................................................................. 1 Theoretical Framework ............................................................................................................... 2 Problem Statement ...................................................................................................................... 5 Professional Significance ............................................................................................................ 6 Purpose........................................................................................................................................ 6 Limitations ................................................................................................................................... 7 Definitions ................................................................................................................................... 7 Organization of the Study .......................................................................................................... 10 Summary .................................................................................................................................... 10

Chapter 2 ................................................................................................................................... 12

Review of the Literature ............................................................................................................ 12 Associate’s Degree Agricultural Programs .............................................................................. 12 Defining Problem Solving ......................................................................................................... 14 Problem Solving Processes ....................................................................................................... 15 Problem Solving Models ........................................................................................................... 16 Problem Solving In the Classroom ............................................................................................ 17 Problem Solving Style ............................................................................................................... 18 VIEW: An Assessment of Problem Solving Style TM .................................................................. 19 Learning Styles .......................................................................................................................... 23 Learning Style Defined .............................................................................................................. 24 Field Dependence ...................................................................................................................... 25

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Field Independence ................................................................................................................... 26 Learning Style Implications ...................................................................................................... 27 Learning Style and Teaching..................................................................................................... 28 Group Embedded Figures Test ................................................................................................. 30 Learning Style and Problem Solving Style ................................................................................ 31 Summary .................................................................................................................................... 31

Chapter 3 ................................................................................................................................... 32

Methodology ............................................................................................................................. 32 Variables ................................................................................................................................... 32 Context of the Study ................................................................................................................... 33 Institutional Review Procedures. .............................................................................................. 33 Place. ......................................................................................................................................... 33 Time. .......................................................................................................................................... 33 Research Design ........................................................................................................................ 34 Research Objectives .................................................................................................................. 35 Population ................................................................................................................................. 35 Participants ............................................................................................................................... 35 Instrumentation ......................................................................................................................... 36 VIEW: An Assessment of Problem Solving Style TM .................................................................. 36 Group Embedded Figures Test. ................................................................................................ 38 Procedures ................................................................................................................................ 39 Data Analysis ............................................................................................................................ 40 Summary .................................................................................................................................... 41

Chapter 4 ................................................................................................................................... 43

Results ....................................................................................................................................... 43 Objective One: Determine if there is a difference in problem solving styles between the Virginia Polytechnic Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and grade point averages. ........ 43 Objective Two: Determine if there is a difference in learning styles between the Virginia Polytechnic Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and grade point averages. ............... 50 Objective Three: Determine if there is a relationship between problem solving style and learning style. ............................................................................................................................ 52

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Objective Four: Explain students’ end of course grades using problem solving and learning styles. ......................................................................................................................................... 60 Summary .................................................................................................................................... 61

Chapter 5 ................................................................................................................................... 63

Discussion of Findings, Conclusions and Implications ............................................................ 63 Discussion of the Findings ........................................................................................................ 63 Objective One: Determine if there was a difference in problem solving styles between the Virginia Polytechnic Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades. ... 63 Objective Two: Determine if there is a difference in learning styles between the Virginia Polytechnic Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades. .......... 65 Objective Three: Determine if there is a relationship between problem solving style and learning style. ............................................................................................................................ 66 Objective Four: Explain students’ end of course grades using differences in student and teacher problem solving and learning styles. ............................................................................ 67 Conclusions ............................................................................................................................... 67 Recommendations ...................................................................................................................... 67 Recommendations for practice. ................................................................................................. 68 Recommendations for research. ................................................................................................ 68 Summary .................................................................................................................................... 70 References ................................................................................................................................. 71

Appendix A ............................................................................................................................... 75

Appendix B ............................................................................................................................... 77

Appendix C ............................................................................................................................... 79

Appendix D ............................................................................................................................... 82

Appendix E ................................................................................................................................ 86

Appendix F ................................................................................................................................ 90

Appendix G ............................................................................................................................... 92

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Appendix H………………………………………………………………………………….. 98

Appendix I ............................................................................................................................... 100

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List of Tables

Table Page

Table 1 Problem Solving Style . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Table 2 2007 VIEW: An Assessment of Problem Solving Style TM Descriptive Statistics (N=16,141) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Table 3 Intercorrelations of VIEW Dimensions, Age, and Gender (N=10,151) . . . . . . . . 38

Table 4 Number Correct: GEFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Table 5 Teacher Problem Solving Style Summary Statistics (n=6). . . . . . . . . . . . . . . . . . 44

Table 6 Student Problem Solving Style Summary Statistics (n=93) . . . . . . . . . . . . . . . . . 44

Table 7 Orientation to Change Summary Statistics (N=99). . . . . . . . . . . . . . . . . . . . . . . 45

Table 8 Manner of Processing Summary Statistics (N=99) . . . . . . . . . . . . . . . . . . . . . . 47

Table 9 Ways of Deciding Summary Statistics (N=99) . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Table 10 Learning Style Summary Statistics (N=99) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Table 11 Students and Teachers Problem Solving and Learning Styles Intercorrelations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Table 12 Degree Program Problem Solving and Learning Styles Intercorrelations . . . . 54

Table 13 Academic Year Problem Solving and Learning Styles Intercorrelations. . . . . . 55

Table 14 Problem Solving and Learning Styles Intercorrelations Between Age Groups . 57

Table 15 Problem Solving and Learning Styles Intercorrelations Between GPA Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Table 16 Regression Analysis to Explain End of Course Grades (N = 440). . . . . . . . . . . 61

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List of Figures

Table Page

Figure 1 Cognitive Function Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Figure 2 Cognitive Effect and Behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Figure 3 VIEW: An Assessment of Problem Solving Style TM Theoretical Framework . 20

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Chapter 1

Introduction

This study outlines the impact student and teacher cognitive style had on the end of

course grades of students enrolled in the Virginia Polytechnic Institute and State University

Agricultural Technology Program during the 2006 and 2007 fall semesters. The Virginia

Polytechnic Institute and State University Agricultural Technology Program is a two year

agricultural associate’s degree program at one of Virginia’s Land Grant universities. Cognitive

style is best described as “a preferred way of thinking” (Grigorenko & Sternberg, 1997, p. 297).

Cognitive style, or thinking style, has many different components and this study closely

examined problem solving style (orientation to change, manner of processing and ways of

deciding) and learning style (field dependence/independence). Problem solving style, a

component of cognitive style, is defined as one’s personal preferences in regards to novel

thoughts and ideas, handling change, and how one effectively manages ill-structured and

complex opportunities and challenges (Selby, Treffinger, Isaksen, & Lauer, 2004). Sarasin

(1999) described learning style as “as a certain specified pattern of behavior and/or performance

according to which the individual approaches a learning experience, a way in which an

individual takes in new information and develops new skills, and the process by which the

individual retains new information or new skills” (p. 1).

Cognitive style has a significant role in the methods and procedures teachers choose to

use in the classroom. Teachers tend to teach content in a manner that is reflective of their

personal cognitive style (Dunn & Dunn, 1979; Gregorc, 1979; Raven, Cano, Carton, &

Shelhamer, 1993; Witkin, 1973). As instructors become aware of their own and their students’

cognitive style, they can make moves to adjust and accommodate each individual’s natural

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learning tendencies. Keefe (1988) illustrated that most students do not know how they learn or

think or how to ensure maximum and efficient learning. A number of researchers drew

conclusions that directly tied learning style to academic achievement (Dunn & Dunn, 1979;

Gregorc, 1979; Grigorenko & Sternberg, 1997; Sarasin, 1999).

Theoretical Framework The theoretical framework for this study was based upon the works of Michael Kirton

and his Cognitive Function Schema (Figure 1).

Figure 1. Cognitive Function Schema. (Kirton, 2003, p. 36) Permission granted from publisher

in Appendix A.

There are three distinct components of this model: cognitive function, behavior and

environment. The environment has a distinct impact on how individuals think. The

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environmental impacts are largely situational and difficult to control. Environment influences the

way information is interpreted and processed as well as how decisions are made. This component

of the model is responsible for both culture and climate and is the domain in which individual

decisions are carried out (Kirton, 2003). Climate is influenced by nine factors; (a) challenge and

involvement, (b) freedom, (c) idea-time, (d) idea-support, (e) playfulness/humor, (f) debate, (g)

conflict, (h) trust and openness, and (g) risk-taking (Isaksen and Akkermans, 2007). Isaksen and

Akkermans (2007) consider culture to have five components including: (a) traditions, (b) beliefs,

(c) values, (d) history, and (e) customs.

Cognitive function is comprised of three components: cognitive resource, cognitive

affect, and cognitive effect. An individual’s cognitive resource level is reflective of their unique

level of ability. Cognitive resources are accumulated via problem solving processes associated

with learning and are retrieved for use by memory (Kirton, 2003). Cognitive resources may be

contextual, depending on an individual’s knowledge, skills and prior experiences. Attitudes and

opinions impact our cognitive function in the affective domain. An individual operates in

cognitive affect when under the influence of their motive, they choose a problem to be solved

and determine the necessary outputs of the problem solving process (Kirton, 2003). One’s belief

and value system may play a powerful role in making most choices; subconsciously or

consciously. This component of the model is what guides an individual in subconsciously

selecting an appropriate type of solution to the perceived problem (Kirton, 2003). Cognitive

effect is the process of beginning in the problem solving process while incorporating an

individual’s style and mental capacity or potential cognitive level into the equation (Kirton,

2003). In other words, cognitive effect is how an individual prefers to think or function at the

cognitive level. Cognitive effect is a natural tendency; during which thinking, learning and

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problem solving will be a fluid and comfortable process for the individual. Style comes into the

equation in this domain. Style is synonymous with preferred thinking methods. It should be

noted that an individual’s cognitive function is subject to change overtime, however this change

must not be rapid or inexistent (Kirton, 2003).

The product of our environment and cognitive function is behavior. Our behavior is

largely a result of preference; if the environment is pleasing and an individual’s preferred

methods of problem solving style and natural thought process have been promoted. A coping

behavior is observed when an individual must act outside of their preferred cognitive style

(Kirton, 2003). Coping behaviors have the potential to be physically and psychologically

damaging if experienced long term. The behavior and environment components interact when

feedback is collected and analyzed (Kirton, 2003). The impact of this feedback is only a

component of cognitive function when an individual allows to let it be (Kirton, 2003).

This study is specifically focused on the relationship between cognitive effect stylistic

preferences and behavior. As previously mentioned, style preferences are a central component of

the cognitive effect domain. The relationship between problem solving and learning style is

outlined in figure 2.

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Figure 2. Cognitive Effect and Behavior.

Problem Statement

There are many factors that affect student performance in the classroom and cognitive

style is one of them. Studies have evaluated the impact that preferred teacher and student

learning styles have on student achievement (Dunn & Dunn, 1979; Gregorc, 1979; Grigorenko &

Sternberg, 1997; Sarasin, 1999). The researcher found no literature linking problem solving

styles (orientation to change, manner of processing, and ways of deciding) to perceptual learning

styles. The researcher did not find literature indicating the impact teacher and student problem

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solving or learning styles have on the course grade performance of students enrolled in

agricultural associate’s degree programs at land grant institutions.

Professional Significance

This study grants an opportunity to provide valuable descriptive knowledge to study

participants and the general population of students and teachers involved in the Virginia

Polytechnic Institute and State University Agricultural Technology Program. Identifying and

explaining the relationship cognitive style has on end of course grades will provide especially

valuable information to the program’s instructors and administrators. This study will serve as a

reminder to program instructors on the importance of cognitive style awareness in the classroom.

Prior researchers have found that the closer the student’s learning style was to the

instructor; the more probable it was that they would have higher levels of academic achievement.

Despite research linking learning style to academic achievement, a significant void exists of

literature related to end of course grades and problem solving style (Luk, 1998). This study has

the potential to contribute to the body of literature regarding cognitive style. It will also help

explain the influence cognitive effect has on behavior. In addition, this study will help meet the

National Research Agenda for Agricultural Education and Communication objective RPA-2 by

addressing the research question, “What factors are predictive of student success in college?”

(Osborne, n.d.).

Purpose

The central focus of this study was to examine and identify student and teacher cognitive

style and its relation to students’ end of course grades in the Virginia Polytechnic Institute and

State University Agricultural Technology Program. Specifically, the researcher sought to:

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1. Determine if there is a difference in problem solving styles between the Virginia Polytechnic Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and grade point averages;

2. Determine if there is a difference in learning styles between the Virginia Polytechnic

Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and grade point averages;

3. Determine if there is a relationship between problem solving style and learning style; and

4. Explain students’ end of course grades using problem solving and learning styles.

Limitations

All conclusions and implications drawn from this study were subject to certain confines.

Data collections were limited to those obtained from enrolled students and instructors in the 2008

spring semester of the Virginia Tech Agricultural Technology program. Any potential

generalizations as a result of this study’s outcomes are subject to the degree of similarity that

students and teachers have to those in the study.

Definitions Agricultural Associate’s Degree Program: an agriculturally based academic postsecondary

collegiate level program. Graduates earn an associate’s degree.

Cognitive Style: “a preferred way of thinking”, also referred to as thinking style (Grigorenko &

Sternberg, 1997, p. 297).

Course Grade: an accepted manner for quantitatively assessing an individual’s cumulative

academic performance during the length of a course. The course grade is on a scale from 0 to

100 points.

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Field Dependence: a learning style categorization for an individual, who is extrinsically

motivated, learns well in a social environment, emphasizes the value of relationships, view the

world globally and need external goals, guidance and assistance (Dunn & Dunn, 1979; Luk,

1998; Raven et al., 1993; Witkin, Moore, Goodenough, & Cox, 1977).

Field Independence: a categorization for an individual who has strong analytic skills, high levels

of ambition, is able to organize and restructure difficult material, remains uninfluenced by social

trends and relies on assistance and direction from authority figures (Luk, 1998; Raven et al.,

1993; Witkin et al., 1977). Field Independent learners are typically high achieving students (Luk,

1998).

First Year Student: a designation for a typical freshmen student enrolled in the Agricultural

Technology program at the Virginia Polytechnic Institute and State University. First year

students have the same or very similar course schedules and often have very similar life

experiences.

Land Grant University: universities founded as a result of the 1862 and 1890 Morrill Acts. The

purpose behind these acts was to make education available to the working class by establishing a

system for states to appropriate funding and land for state colleges (Gordon, 2003). Virginia

Polytechnic Institute and State University is one of Virginia’s land grant universities.

Learning Style: a person’s instinctive and preferred manner of engaging in learning endeavors;

Sarasin (1999) defined learning style as, “a certain specified pattern of behavior and/or

performance according to which the individual approaches a learning experience, a way in which

an individual takes in new information and develops new skills, and the process by which the

individual retains new information or new skills” (p. 1).

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Manner of Processing: one component of an individual’s overall problem solving style;

specifically describes an individual’s favored methods of processing and handling information

during the problem solving process (Selby et al., 2004).

Orientation to Change: one component of an individual’s overall problem solving style;

specifically how a person prefers to react to change in their life and to what extent creative

means are used to manage that change (Treffinger & Selby, 2004).

Problem Solving: a mental process that an individual uses to arrive at the best possible solution

to both conscious and unconscious decisions that is subject to a set of restrictions (Woods, 1987).

Problem Solving Style: representative of one’s personal differences in regards to the reaction to

novel thoughts and ideas, handling change, and how one effectively manages ill-structured and

complex opportunities and challenges (Selby et al., 2004).

Second Year Student: a designation for a student who has completed their first year of course

work and continues their enrollment in the Agricultural Technology program at the Virginia

Polytechnic Institute and State University. Second year students tend to have a broad range of

experiences to draw upon, including coursework and an internship that is traditionally between

their first and second year. These students are in their last year of school before graduating.

Ways of Deciding: one component of an individual’s overall problem solving style; addresses

whether an individual is influenced by the task at hand or other people’s opinions and emotions

when making decisions (Selby, Treffinger, Isaksen, & Lauer, 2002).

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Organization of the Study

Chapter 1 contains the introduction, theoretical framework, problem statement,

professional contributions of the study, purpose of the study, research objectives, limits of the

study and definitions of key terms.

Chapter 2 includes the review of literature pertinent to this study. It contains information

about agricultural associate’s degree programs, problem solving, problem solving style, learning

style and the impact of problem solving and learning style on academic achievement. This

chapter also has information about VIEW: An Assessment of Problem Solving Style TM and the

Group Embedded Figures Test (GEFT).

Chapter 3 outlines the methods used to conduct this research study. It includes the

variables of the study, context in which it takes place, the research design, research questions,

and descriptions of the population, subjects, instruments, procedures and data analysis. This

section also contains a descriptive analysis of the population and the instruments used in this

study.

Chapter 4 offers a means of displaying the results of the study. It includes an analysis of

each research objective. The last section, Chapter 5, is a discussion of the findings and implies

practical applications for the results of the study and concerns for future research.

Summary

This study sought to determine and explain the relationship between students’ and

teachers’ cognitive style in regard to students’ end of course grades while enrolled in the

Agricultural Technology Program at Virginia Polytechnic Institute and State University. This

chapter provided a descriptive outline of the study.

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The reasons that make this study justifiable and significant were in this chapter. Lastly,

important definitions were included.

The objectives of this project were clearly outlined. They included:

1. Determine if there is a difference in problem solving styles between the Virginia Polytechnic Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades;

2. Determine if there is a difference in learning styles between the Virginia Polytechnic

Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades;

3. Determine if there is a relationship between problem solving style and learning style; and,

4. Explain students’ end of course grades using problem solving and learning styles.

Chapter 2 provides the theoretical and empirical research relevant to this study.

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Chapter 2

Review of the Literature

This chapter describes the theoretical and empirical research relevant to this study.

Literature was reviewed in the following areas: (a) associate’s degree agricultural programs, (b)

problem solving processes and methods, (c) problem solving style, (d) learning style, and (e)

student and teacher cognitive style and its relationship to academic performance.

The review of the literature revolved around publications in the education and corporate

business literature; doctoral dissertations; proceedings from educational research meetings; ERIC

documents; textbooks; and websites.

Associate’s Degree Agricultural Programs This study was concentrated on a highly specified sample of students enrolled in the

agricultural associate’s degree program at Virginia Polytechnic Institute and State University.

There was little literature available to describe students and teachers in similar programs.

In a study conducted by Mykerezi, White, and Crunkilton (2007) the perceptions of

directors of agricultural associate’s degree programs at land grant universities were measured.

These programs are similar in nature to the program on the campus of Virginia Polytechnic

Institute and State University. An overwhelming majority of these programs have credits that

directly transfer to four year bachelor’s degree programs (Mykerezi et al., 2007). Most programs

of this nature face excessive demands from agricultural industries for graduates (Mykerezi et al.,

2007).

The specific program in this study is located on and has access to many resources at one

of Virginia’s land grant institutions, Virginia Polytechnic Institute and State University. The

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program has six core faculty and a variety of instructors from other departments in the

University’s College of Agriculture and Life Sciences. Students that are enrolled in the program

are in one of two options; applied agricultural management or landscape and turf management.

Each option incorporates small business management courses as well as core courses that are

domain and career specific.

As postsecondary students, these individuals tend to have certain characteristics of adult

learners. Adult learners behave like adults, have adult oriented responsibilities, and consider

themselves as an adult (Knowles, 1980). In this particular context, Knowles (1980) classifies

adult learners as youthful individuals who are responsible for their behaviors outside of the

classroom and claim responsibility for their behavior in school as adult learners. Adult oriented

learners will have a high motivation to learn, focus their energies on learning material that is

applicable out of school, believe that experiential learning is very effective, and their learning,

thinking and problem solving style is likely to have matured as they have aged (Sarasin, 1999).

In Knowles’ (1984) adragogy model of adult learning, there were six components: (a) adults

need to know why they need to learn something, (b) the learner’s self concept of being

responsible for their choices and livelihood, (c) the learner’s experience is valuable in adult

teaching, (d) adults are ready to learn something readily applicable to their current life situation,

(e) adults possess a life-centered approach to learning, and (f) adults are intrinsically motivated

learners. A program with adult learners does need to be sensitive and flexible to student

experience in order to accommodate the vast knowledge that students may possess prior to

enrollment. The Agricultural Technology program at Virginia Polytechnic Institute and State

University does acknowledge that students’ experience may be comparable to that of academic

courses or credits (Harrington, 1987). Adult centered andragogy can provide an industry

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appropriate knowledge base, local and global industry literacy, and facilitate decision making

(Flora, 1987).

The population of students enrolled in the Virginia Polytechnic Institute and State

University Agricultural Technology program is very unique, especially in comparison to other

learners at the University. Students enroll in the program for a variety of reasons. These

programs are typically not research centered and have trouble allocating both federal and state

financial resources (Mykerezi et al., 2007). Agricultural associate’s degree programs tend to

have certain pedagogical trends. The content is often more applied and kinesthetically oriented

compared to the theoretically based instruction in four year bachelor’s degree agricultural

programs (Mykerezi et al., 2007). There are six instructors in the Agricultural Technology

program with a full time teaching assignment. Other instructors are college faculty that belong to

other departments and only teach part-time in the Agricultural Technology program (Mykerezi et

al., 2007).

Defining Problem Solving

Problem solving skills are essential to a meaningful life. In order to capitalize on both

problem solving methods and strategies, one must be conceptually familiar with problem

solving. After a solid foundation is built in problem solving it is possible to better understand and

apply problem solving styles in one’s daily lifestyle habits. As a result, problem solving and

problem based learning have been introduced to educational institutions. Psychological literature

has defined problem solving, justified its value to educators and students, and established how to

best incorporate problem solving into today’s classrooms (Berardi-Coletta, Buyer, Dominowski,

& Rellinger, 1995; Reder & Ritter, 1992). Information has been retrieved from cognitive

psychologists and combined with work from problem solving experts to develop

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recommendations on integrating problem solving in the best possible manner (Bransford &

Stein, 1984; Mayer, 1983; Allen Newell & Simon, 1972).

Despite the overwhelming support for integrating problem solving in the classroom, this

teaching method is difficult for instructors to implement (Knowlton, 2003). In fact, the majority

of college professors consider their teaching to be effective, despite what contrary evidence they

may be provided (Paul, 2005). Problem solving in education is a rapidly changing concept. It has

evolved from a universal systematic process in the 1900s to a complex set of steps with different

learner specific attitudinal domains (Foshay & Kirkley, 1998). Many instructors avoid problem

solving teaching methods because of preconceived notions that it comes at the cost of developing

cognitive knowledge. Foshay and Kirkley (1998) discussed how a learner’s problem solving

ability cannot rise above the level of their declarative knowledge. A student can possess

imminent knowledge, yet still lack problem solving skills, but they cannot possess problem

solving skills without at least adequate levels of knowledge. At the end of the day, problem

solving is more cost-effective for educators because it helps learners adapt better without

needing further formal training (Foshay & Kirkley, 1998). Once one agrees that problem based

learning is an effective teaching strategy, it is appropriate to explore scientific based processes

and methods of solving problems.

Problem Solving Processes

Much of the work in problem solving for this study is grounded in the field of cognitive

psychology. A great deal of research within psychology has been concerned with problem

solving (Bransford & Stein, 1984; Davis, 1966; Gagne, 1964; Maier, 1970; A. Newell, Shaw, &

Simon, 1958; Polya, 1946). After the initial decision to adopt problem solving skills in the

learning environment, it is important to develop a model to follow when solving problems. The

16  

literature related to cognitive problem solving processes has addressed multiple models to

explain the problem solving process. The models are based on the work of Polya, Pietrasinski,

Bransford and Stein, Lockhead and Whimbey. The early literature is based around the principals

of artificial intelligence and mathematics; since then, the theories have been transferred to

education.

Before explaining the models of problem solving it is imperative that problem solving is

defined. From the literature many definitions of problem solving have been identified.

Definitions of problem solving have included key concepts such as tasks, goals, mental

processes, and attaining goals. Bloom and Broder (1950) defined problem solving as “the

process by which the subject goes from the problem or task as he sees it to the solution which he

regards as meeting the demands of the problem” (p. 7). Polya (1946) defined problem solving as

“finding a way out of a difficulty, a way around an obstacle, attaining an aim that was not

immediately attainable” (p. ix). Later, Woods (1987) defined problem solving as “the mental

process that we use to arrive at ‘best’ answer to an unknown or some decision, subject to a set of

constraints” (p. 55). Ricketts (1997) simply explained problem solving as a process of initializing

corrective actions in order to reach a specified goal.

Problem Solving Models

The literature related to cognitive problem solving process has addressed multiple models

to explain the problem solving process. The models are based on the work of Polya, Pietrasinski,

Bransford and Stein, Lockhead and Whimbey. Polya (1946) utilized mathematics as the vehicle

to develop and describe the problem solving process as four phases the learner must do in order

to solve problems. The four phases are: (a) understand the problem, (b) make a plan, (c) carry out

17  

the plan, and (d) look back at the solution. Polya’s work was primarily in the field of

mathematics and based the theory on making individuals think.

While working on theories of efficient thinking, Pietrasinski (1969) developed a four step

problem solving model. His model operated on these four foundational steps; (a) confrontation

by a problem, (b) search for the solution, (c) the solution of the problem or the admission of

failure, and (d) final checking of and perfecting the solution. Pietrasinski’s straightforward model

is depictive of a linear approach to solving problems.

Building on the concepts of Polya, Bransford and Stein (1984) developed a very similar

model of problem solving, titled IDEAL. The model IDEAL included five steps (a) identify the

problem, (b) define and represent the problem, (c) explore possible strategies, (d), act on the

strategies, and (e) look back and evaluate the effects of your activities. Bransford and Stein’s

IDEAL problem solving model paved is one of the most recent models of problem solving.

While very similar to Pietrasinski’s model, Lockhead and Whimbey (1987) developed a

four step model using research to compare experienced and novice problem solvers. After

analysis, the expert problem solver uses these four steps: (a) the expert assembles information

from the problem, (b) plans the problem solution, (c) solves the problem, and (d) checks the

solution. When this model is applied for novice learners, frequent failure is a result of the lengthy

process involved in becoming an expert problem solver (Lockhead & Whimbey, 1987).

Problem Solving In the Classroom

When incorporating problem solving into the classroom, there are approximately nine

skills that are directly related to a student’s ability to solve problems. Students will develop,

contextual knowledge of the problem area, abilities to synthesize data and evaluate it’s worth,

facilitate learning on their own time, reasoning content in both breadth and depth, motivation and

18  

perseverance to tackle tough issues, skills with handling stress and procrastination, inter-personal

and intra-personal skills, communication skills, and the ability to reflect critically on their

thinking style and predispositions (Woods, 1987). All of these skills and dispositions are present

in the ideal learning environment. Students involved in problem solving are better at self study

skills and have a higher level of motivation to succeed than do students who are not enrolled

courses with problem solving elements (Savin-Baden & Major, 2004). Courses with problem

solving activities often incorporate debates, case-studies, reflective assignments, journals, and

class discussions. Due to the nature of the class, learners are no longer able to take a passive role

in their learning. In the educational world, passive instruction is inferior to methods that actively

engage the students (Paul, 2005). The shift from observer to participant changes the state of

learning for students involved in problem solving (Savin-Baden & Major, 2004). Students that

are actively engaged are more likely to learn material. By using problem solving in the

classroom, teachers help develop key life skills, attitudes and cognitive development of students.

Problem Solving Style

Problem solving style is representative of one’s personal differences in regards to their

reaction to novel thoughts and ideas, handling change, and how one chooses to effectively

manage ill-structured and complex opportunities and challenges (Selby et al., 2004). When

working alone or in groups, people tend to typically attempt to solve problems in one of two

ways. People tend to either attempt to modify the problem and make it better by enhancing parts

of the problem or they tend to drop what has been done in the past and develop novel solutions

(Treffinger & Selby, 2004). No matter which venue is chosen, successful problem solving will

likely resort if an individual consciously or subconsciously chooses to follow strategies or

19  

models outlined previously. Treffinger, Selby, Isaksen, and Crumel (2007) described problem

solving style by defining exactly what problem solving style is and is not, as outlined in Table 1.

Table 1

Problem Solving Style

Problem solving styles are: Problem Solving Styles are NOT:

• Natural (everyone has a style) • Fixed, inflexible (people can adapt)

• Neutral (no “right or wrong style”) • Measures of one’s ability

• Ways you prefer to behave • Excuses for not doing well

• Comfortable (the way one really is) • Ways people expect others to behave

• Stable (does not change rapidly) • Rules for how one has to behave

• Strengths (how one is at their best) • “Faults” (things one cannot do well)

Note: This table was reproduced with permission from Treffinger et al. (Appendix B), as seen in

An Introduction to Problem-Solving Style (2007, p. 3).

VIEW: An Assessment of Problem Solving Style TM attempts to quantify a person’s

problem solving style in three independent domains; orientation to change, manner of processing

and ways of deciding. Each domain classifies an individual on a numerical continuum. No style

typology is more preferable or less desirable than the next.

VIEW: An Assessment of Problem Solving Style TM

The VIEW: An Assessment of Problem Solving Style TM instrument is grounded in the

literature. An example of the instrument can be found in Appendix C. The instrument’s

theoretical framework is pictured in figure 2.

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Figure 3. VIEW: An Assessment of Problem Solving Style TM Theoretical Framework. This

figure was reproduced with permission from Treffinger et al. (Appendix B), as seen in An

Introduction to Problem-Solving Style (2007, p. 3).

Before discussing problem solving style in great detail, it is important to first review the

foundational concepts this theory is built upon. Learning style plays a vital role in one’s problem

solving style. Many researchers, including Rita and Kenneth Dunn, agree that learners typically

prefer structure or are limited by it; they may need authority figures in close proximity during

learning while others prefer that authority be distant (Selby, Treffinger, & Isaksen, 2007).

Literature indicates that learning is subject to many elements. Learning can happen with or

without certain elements present. However, to maximize learning potential, preferred elements

will be present during learning (Selby et al., 2007).

In regards to the problem solving model, cognitive style is based on the concept of the

independent constructs of personality and cognition domains actually intersecting (Selby et al.,

21  

2007). In a study by Isaksen, Lauer and Wilson (2003), a significant correlation was found

between the Kirton Adaption Innovation Inventory (KAI) and the Myers-Briggs Type Indicator

(MBTI) personality assessment. The MBTI area of Sensing-Intuitive accounts for 30% of KAI

variance. The Judging-Perceiving domains explain 19% of KAI variance (Isaksen et al., 2003).

The third foundational concept of problem solving style is built upon the personality type

work of Carl Jung. Jung theorized that personality could be measured on several different

degrees; sensation-intuition, thinking-feeling, and judging-perceiving (Selby et al., 2004).

Lawrence (1993) indicated that learning style and personality type overlap; in a similar manner

to that of learning style and cognitive style’s overlapping relationship. Lawrence (1993) reported

that over 80% of researchers studying learning style and personality style have linked various

aspects of the MBTI to a certain learning style. With solid empirical evidence, problem solving

style evolved from work in the distinct fields of learning style, cognitive style, and personality

type. These three components interact together within the cognitive arena.

The developers of VIEW: An Assessment of Problem Solving Style TM based their work

on research done by Cattell, Dunn and Dunn, Gough, Jung, Kirton, Costa and McCrae, Eysenck,

“Five Factor” personality theorists, and on creativity literature (Selby et al., 2007). An

instrument was developed to indicate ones’ personal preference for problem solving styles called

VIEW: An Assessment of Problem Solving Style TM. It identifies problem solving style in three

dimensions; orientation to change, manner of processing and ways of deciding.

An individual’s problem solving style, in regards to their orientation to change,

specifically revolves around their preferences for handling change and what creative solutions

are used to manage it (Treffinger & Selby, 2004). Selby et al. (2007) define this dimension as

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one’s “disposition and preferences for responding to and managing structure, novelty, and

authority, when you are dealing with change or solving problems” (p. 24). Based upon the

assessment results, an individual will lie somewhere on a continuum, between Explorers and

Developers. Explorers, which fall below the mean, are adventurous individuals who enjoy

chasing possibilities and take pleasure in novel and indistinct problems with original, raw and

revolutionary solutions (Selby et al., 2004). Developers prefer a systematic manner of solving

problems, they automatically collect data and synthesize problems in an organized manner that

creates solutions that are deemed valuable and helpful by others (Treffinger & Selby, 2004).

While orientation to change deals with a person’s method of handling change, manner of

processing describes an individual’s preferred methods of handling information during the

process of solving a problem (Selby et al., 2004). Selby et al. (2007) define this dimension as

one’s “dispositions and preferences for how and when you use your own inner energy and

resources, the energy and resources of others, and the environment; and for different ways of

handling information when managing change or solving problems” (p. 27-28). External problem

solvers’ scores fall below the mean and tend to indicate an individual who is extroverted, finds

energy from others, enjoys discussing problems and constructing solutions in groups, and leans

on authority figures for direction and advice (Treffinger & Selby, 2004). Those who score below

the mean on the manner of processing continuum are referred to as external problem solvers. The

opposite of external methods of processing information is internal methods of processing

information. Internal processers tend to be introverted; looking inwards for a solution by

personally reflecting quietly and at a self directed pace (Treffinger & Selby, 2004).These

individuals tend to evaluate their ideas before expressing them publicly, spend considerable time

in reflection and enjoy learning in solitude (Treffinger & Selby, 2004).

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The final dimension of the VIEW: An Assessment of Problem Solving Style TM

assessment quantifies an individual’s ways of deciding. This continuum between people or task

directed decisions is indicative of how a person tends to make decisions and evaluate options

(Selby et al., 2002). Selby et al. (2007) define this dimension as one’s “dispositions and

preferences for balancing and emphasizing task concerns and personal or interpersonal needs

when focusing your thinking and moving toward decisions and action” (p. 30). People that tend

to habitually consider a decision’s outcomes in terms of the potential impact on other individuals

and their emotions are identified as focusing on people (Selby et al., 2002). When people do

habitually base their decisions on other people’s opinions and emotions, their score on the ways

of deciding continuum tends to be below the mean. When an individual falls above the mean,

they are identified as task motivated and tend to rely on logical, sound and easily justifiable

decisions that will easily direct them to an emotion free and defendable solution (Treffinger &

Selby, 2004).

In summary, the VIEW: An Assessment of Problem Solving Style TM instrument

identifies three independent dimensions of problem solving style. The explorer/developer

continuum indicates a person’s orientation to change. The second continuum, external/internal,

estimates an individual’s preferred means of processing a problem. This is indicative of how a

person habitually solves problems. The ways of deciding, or otherwise known as the people/task

continuum assesses an individual’s attention towards emotional or logical outcomes.

Learning Styles

There is no magic formula for predicting student academic success. Nearly all researchers

indicate that everyone has preferred ways to engage in learning (Cano & Garton, 1994; Dunn &

Dunn, 1979; Grigorenko & Sternberg, 1997; Luk, 1998; Sarasin, 1999; Witkin et al., 1977). In

24  

addition, many educational psychologists and researchers also readily identify a student’s ability

as a large contributor to individual academic success (Cano & Garton, 1994; Dunn & Dunn,

1979; Grigorenko & Sternberg, 1997; Sarasin, 1999). Dunn and Dunn (1979) suggest a number

of different factors can cause any individual to react differently to an environment, including:

age, ability, socioeconomic status, or achievement level. In an attempt to explain other variables

contributing to academic achievement, researchers have explored cognitive style (Grigorenko &

Sternberg, 1997). Cognitive style or thinking style is defined as “a preferred way of thinking”

(Grigorenko & Sternberg, 1997, p. 297). Research indicates that style is derived from both

genetic predispositions and the environment and culture in which an individual develops

(Gregorc, 1979).

Learning Style Defined

Learning style is one component of cognitive style. There are many definitions of

learning style. Gregorc (1979) illustrated learning style as, “distinctive behaviors which serve as

indicators of how a person learns from and adapts to his environment” (p. 234). Garger and

Guild (1984) defined learning style as the, “stable and pervasive characteristics of an individual,

expressed through the interaction of one’s behavior and personality as one approaches a learning

task (p. 11). Cano and Garton (1994) wrote that learning style can be simply defined as “the

manner in which learners sort and process information” (p. 6). Dunn (1996) explains that

learning styles are complex reactions to stimulus including emotions, events, and routines. For

the purpose of this study, Sarasin’s (1999) working definition will be used. Sarasin (1999)

defined learning style “as a certain specified pattern of behavior and/or performance according to

which the individual approaches a learning experience, a way in which an individual takes in

new information and develops new skills, and the process by which the individual retains new

25  

information or new skills” (p. 1). It is commonly accepted that every individual has a unique way

of learning and mastering material (Dunn, 1996; Dunn & Dunn, 1979; Gregorc, 1979; Raven et

al., 1993).

Teaching and learning styles are relatively steadfast and resistant or difficult to change

(Dunn & Dunn, 1979; Grigorenko & Sternberg, 1997; Witkin et al., 1977). However, individuals

can cope and adjust learning styles on a situational basis (Gregorc, 1979). Grigorenko and

Sternberg (1997) illustrated that there are no styles that are superior; the focus is to uncover and

develop certain styles for certain circumstances. Despite their relative steadiness, learning styles

are subject to small changes in either direction over time (Dunn & Dunn, 1979). It has been

documented that at between 24 years of age and old age, individuals do tend to become more

field independent (Witkin, Oltman, Raskin, & Karp, 1971).

Field dependence and field independence are the most widely studied aspects of learning

style (Cano & Garton, 1994; Witkin et al., 1977). These indications of learning style only reflect

an individual’s tendency to perceive a stimulus as discrete from its surroundings using passive

global or active and analytical cognitive processes (Luk, 1998; Raven et al., 1993; Witkin et al.,

1977; Witkin et al., 1971). Field dependence and field independence lie on two ends of the

learning style continuum. It should be noted that learning style is not an indicator of learning

ability or skill (Dunn, 1996; Gregorc, 1979; Grigorenko & Sternberg, 1997; Witkin et al., 1977;

Witkin et al., 1971).

Field Dependence

Researchers have clearly defined the identifying characteristics of field dependent and

field independent learners. Field dependent learners tend to be less independent people who seek

extrinsic motivation, rely heavily on authority figures, and have difficulties constructing personal

26  

learning experiences (Luk, 1998). Field dependent learners’ beliefs and actions are strongly

influenced by their social experiences (Luk, 1998; Raven et al., 1993; Witkin et al., 1971). Field

independent and field dependent learners value social relationships differently. Many individuals

can learn in a host of sociological structured activities, but some may only reach acceptable or

optimal performance when specific relationships are fostered (Dunn & Dunn, 1979). These

individuals learn well from social interaction and can selectively remember materials that have

social connotations (Raven et al., 1993; Witkin et al., 1977). Field dependent learners are likely

to need externally defined goals whereas field independent learners readily set their own goals

(Luk, 1998; Witkin et al., 1977; Witkin et al., 1971). These individuals tend to have global

perspectives and will require explicit guidance and instruction in the classroom (Raven et al.,

1993). Field dependent teachers characteristically provide very little negative feedback to their

classroom community or individual students (Garger & Guild, 1984). These teachers also tend to

naturally create a welcoming classroom environment (Raven et al., 1993). In regards to problem

solving ability, field dependent learners typically lack the inherent ability to solve problems

without prior training in problem solving (Witkin et al., 1977). This does not mean that they lack

problem solving styles; it is rather an indicator of ability. Field dependent students tend to be

attracted to vocational areas in education (Witkin et al., 1977).

Field Independence

Field independent learners are typically characterized as analytical, logic oriented, and

capable of restructuring problems. They rely on direction, assistance and knowledge from others

yet remain less influenced by social circumstances than field dependent learners (Luk, 1998).

These individuals perceive items as being distinctly separate from the surrounding field (Raven

et al., 1993). Field independence is considered to be a significant predictor for student

27  

achievement. Field independent students constantly perform better academically than field

dependent students (Luk, 1998). Field independent learners tend to automatically organize,

master, and interpret learning materials that are void of clear internal structure (Witkin et al.,

1977). Teachers that are field independent focus their attention on content material, emphasize

cognitive development during instruction, and tend to guide students instead of directly

delivering material to learners (Cano, Garton, & Raven, 1992; Raven et al., 1993; Witkin et al.,

1971).

Learning Style Implications

There is some controversy over cognitive styles between genders. Witkin et al. (1977)

indicated that beginning at adolescence; there are slight significant differences in field

dependence and field independence of males and females. Witkin et al. (1971) also found distinct

differences between genders. Females are generally more field dependent than men (Witkin et

al., 1977). However, Grigorenko and Sternberg (1997) studied the thinking styles of high school

gifted students and found that the learning styles did not differ across gender, ability levels or

grade level. Dunn (1996) indicated that gifted students were readily capable of performing at

high levels without using their learning style, whereas average or low achievers can perform

significantly better when using their preferred style. Raven et al. (1993) found that preservice

female agricultural teachers tended to be more field independent than the general population of

females.

Students’ learning styles are predictive of their school success (Grigorenko & Sternberg,

1997). As teachers become aware of their learning style and the learning style of each student,

they are better able to prepare individualized instruction, utilize different teaching strategies,

promote different learning strategies, and enhance the likelihood of student success regardless of

28  

learning style (Gregorc, 1979; Witkin et al., 1977). Recognizing style and ability will allow the

instructor to strategically plan appropriate learning engagements that will strengthen each

individual’s ability to learn using different styles.

Learning Style and Teaching

Academic success is an issue on nearly every educator’s radar. When students are

unsuccessful academically, many begin to immediately reason that time on task, motivation, and

content material difficulties are partial causes for failure. However, oftentimes teaching

strategies may directly affect students’ success. Acknowledging the impact that teaching

methods and learning styles have on achievement may promote more effective teaching and

learning. Other than cases of psychological damage, most students do not know learning

operations and procedures or how to manipulate and control them (Keefe, 1988).

There is a direct correlation between teaching style and learning style (Dunn & Dunn,

1979). Researchers suggest that teachers teach in the same manner in which they learned because

they often believe that the means they used to learn material is the most efficient or correct

manner for learning (Dunn & Dunn, 1979; Sarasin, 1999). Other researchers have also found that

an instructor’s learning style correlates with their habitual teaching style (Gregorc, 1979; Raven

et al., 1993; Witkin, 1973). The relationship between teaching style and learning style plays a

role in the success of students at the postsecondary level (Sarasin, 1999).

The impression a teacher has of their own style and the style and behavior of their

students plays an important role in a student’s success. Teachers often overrate the extent to

which their students share similar thinking styles (Grigorenko & Sternberg, 1997). It is necessary

that when planning and delivering instruction, teachers consider learning styles and their impact

on student achievement (Dyer & Osborne, 1996). For example, Dyer and Osborne (1996) found

29  

that field independent learners respond better to problem solving based teaching methods.

Qualitatively speaking, teachers tend to appear to value students who have the same or similar

thinking styles as they have (Grigorenko & Sternberg, 1997). Periodically assessing and varying

the type of assessment will serve more students (Grigorenko & Sternberg, 1997). Diverse

teaching strategies are necessary to accommodate diverse learning styles in every classroom.

Knowledge of teacher and student learning styles is extremely beneficial for both parties

in terms of assessing knowledge and skills. When instructors develop their own assessments, it is

possible to develop a testing tool that lends itself towards one thinking style over another. In a

study by Grigorenko & Sternberg (1997) gifted students performed better on assessments that

matched their preferred thinking style. When taught in a style that compliments theirs, students

not only achieve higher academically, but tend to be more motivated (Dunn & Dunn, 1979;

Gregorc, 1979). In addition to higher academic achievement, at the postsecondary level, students

should know their styles because it can produce positive psychological benefits; including higher

self esteem and confidence as well as an appreciation of diversity (Sarasin, 1999).

Certain career fields tend to attract individuals with certain learning styles (Luk, 1998).

Different career fields require different skills and dispositions for success. It was found that

preservice agricultural instructors tend to be more field independent but tend to teach in a learner

centered manner; a characteristic more typical of field dependent teachers (Raven et al., 1993).

Despite that some fields may attract less style diverse clientele; learners should be exposed to

different learning styles in order to foster the development of thinking skills and appreciation for

diverse cognitive styles.

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At the bottom line, “teachers may be knowledgeable, charismatic, dramatic, hardworking,

caring, and dedicated, and still not be effective with students whose learning styles are not

complemented by their teaching styles” (Dunn & Dunn, 1979, p. 241). Positive transfer is more

likely to occur in students when the instructor is aware of and acts upon their students’ learning

styles (Sarasin, 1999). Students will perform better when allowed to learn in their preferred style.

Group Embedded Figures Test

There are a number of methods available to designate learning style via field dependence

and field independence (Witkin et al., 1977). The Group Embedded Figure Test (GEFT),

developed by Witkin et al. (1971), is the most frequently used tool for identifying field

independence and field dependence (Luk, 1998). The GEFT was developed to replace the

Embedded Figures Test (EFT) because group testing was not possible with the EFT (Witkin et

al., 1971). The concept developed out of the idea that both perceptual and intellectual tasks can

be used to assess very broad domains of an individual’s daily life (Dyer & Osborne, 1996). It has

been described as measuring an individual’s level of abstractness or concreteness on a scale of 0

to18 (Witkin et al., 1971). GEFT has three individual sections. The first section has seven

straightforward items. The second and third sections have nine items that are more complex. The

number of correctly identified simple shapes in the second and third sections is the total GEFT

score (Witkin et al., 1971). Scores that are below the national mean are field dependent while

scores above the national mean are field independent (Luk, 1998; Raven et al., 1993; Witkin et

al., 1971).

GEFT is recognized as a standard test for measuring field dependence and field

independence (Dyer & Osborne, 1996; Witkin et al., 1971). The GEFT has reliability of .82 for

makes and .79 for females (Witkin et al., 1971).

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Learning Style and Problem Solving Style The literature indicates that learning style has an impact on students’ academic

achievement, teachers’ methods of teaching and learning and interactions that take place between

students and teachers (Cano & Garton, 1994; Dunn & Dunn, 1979; Gregorc, 1979; Witkin et al.,

1977). There was no research found on the influence student and teacher problem solving style

and academic performance. There is also very little research exploring the relationship between

field dependence and field independence to problem solving style in terms of orientation to

change, manner of processing, and ways of deciding. However, these two indicators are part of

the larger realm of cognitive style.

Summary

This chapter was meant to review literature related to the cognitive style, specifically

including problem solving and learning style in regards to students and teachers in the

Agricultural Technology Program at Virginia Polytechnic Institute and State University. Problem

solving style and learning style were put into context with cognitive function and behavior in the

Cognitive Function Schema (Kirton, 2003). An extensive background of the cognitive problem

solving and a historical review of problem solving methods were included in this chapter.

Problem solving style was broken down into its three constructs: orientation to change, manner

of processing and ways of deciding. The literature about learning styles and field dependence

and field independence was analyzed and related to student achievement and instructor

pedagogy.

Chapter 3 will introduce the methodology used to conduct this study.

32  

Chapter 3

Methodology

This chapter details the methods that were used to address the proposed research

objectives. The research context, design, population demographics, instruments, and procedures

for data analysis are also included in this section.

Variables The quantitative research methods used in this study were descriptive, correlational and

explanatory. For objectives one and two, the variables were problem solving style (orientation to

change, manner of processing, ways of deciding – VIEW: An Assessment of Problem Solving

Style TM) and learning style (field dependence, field independence – GEFT). Each variable was

compared between groups of the sample for students and teachers, student degree options,

academic years, ages, and GPA ranges.

For objective three, the variables were problem solving style (orientation to change,

manner of processing, ways of deciding – VIEW: An Assessment of Problem Solving Style TM)

and learning style (field dependence, field independence – GEFT). Correlations were made

between problem solving and learning styles for students and teachers, student degree options,

academic years, ages, and GPA ranges.

Objective four’s independent variables were student and teacher problem solving styles

(orientation to change, manner of processing, and ways of deciding – VIEW: An Assessment of

Problem Solving Style TM) and learning styles (field dependence, field independence – GEFT).

The dependent variable was overall course grades.

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Context of the Study

Institutional Review Procedures. Virginia Polytechnic Institute and State University

Institutional Review Board (IRB) policies were followed. The consent form, grade release,

contact and thank you letters and the IRB approval letter are included in Appendix E, F, and G,

respectively.

Place. The entire study was conducted on the Blacksburg campus of Virginia

Polytechnic Institute and State University. The data were collected two different times, one

session for students and one for instructors. Student data were collected in the Personnel

Management Course: AT 0224 (Course Reference Number 10709). Permission from the

Personnel Management instructor was received prior to completing the study (Appendix I).

Instructor data were collected during a bi-weekly faculty meeting. The VIEW: An Assessment of

Problem Solving Style TM was given in an electronic format to all research participants. An

online format for this test was chosen for this study because all students and teachers in the

Virginia Polytechnic Institute and State University Agricultural Technology program are

required to own and have a functional tablet laptop in their possession during every course.

There is a high correlation between the online and paper version of the VIEW: An Assessment of

Problem Solving Style TM; .92 for orientation to change, .92 for manner of processing, and .98

for the ways of deciding construct (Selby et al., 2007). The learning style data were collected in a

hand scored booklet.

Time. The study was conducted on Thursday, February 22 and Friday, February 23, 2008.

Both students and instructors received the same information regarding the study. The first

announcement was sent over email on Monday, February 4, 2008. On Monday and Wednesday

of February 11-13, 2008, a letter was delivered to all prospective participants. Two email

34  

reminders were sent out again during the week of the study. A make-up date was scheduled for

subjects who were absent during data collection. Three individuals did not consent to

participating in the study. One individual’s problem solving and learning style scores were

omitted from data analysis due to failure to comply with instrument instructions. Cumulative

numerical end of course grade data were obtained from instructor records for the fall 2006 and

fall 2007 semesters. Only courses taught by all six full time faculty members were collected.

Grade data were only collected from students who received a final course grade and those

students were enrolled during initial data collection in the spring 2008 semester.

Research Design

This study was descriptive, correlational, and explanatory. Descriptive research only

describes or tells about an event without explaining why or how it came to its present state

(Reaves, 1992). Correlational research attempts to utilize one variable to predict the value of

another variable (Shaughnessy, Zechmeister, & Zechmeister, 2006). Explanatory research

utilizes a regression analysis of independent quantitative variables to predict dependent

quantitative variables (Ott & Longnecker, 2001).

VIEW: An Assessment of Problem Solving Style TM has been completed by over 16,141

individuals (Treffinger, 2008). There is no relationship between age and the manner of

processing and ways of deciding continuums. There is a statistically significant, but very weak

and negligible correlation between age and the orientation to change continuum of problem

solving style (Selby et al., 2007). The relationship lacks practical significance because of the

extremely large sample size. The results from this instrument are considered to be an accurate

indicator of the population regardless of the participants’ age differences.

35  

Research Objectives This research categorized the cognitive style of students and teachers in the Virginia

Polytechnic Institute and State University Agricultural Technology program and sought to

explain the relationships that student and teacher problem solving and learning styles have on

students’ end of course grades by answering the following objectives:

1. Determine if there is a difference in problem solving styles between the Virginia Polytechnic Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades;

2. Determine if there is a difference in learning styles between the Virginia Polytechnic

Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades;

3. Determine if there is a relationship between problem solving style and learning style; and,

4. Explain students’ end of course grades using problem solving and learning styles.

Population

Teachers and students enrolled in the Virginia Polytechnic Institute and State University

Agricultural Technology program during the 2008 spring semester were the population for this

study. Those students enrolled in and the instructors who were teaching in the program were

readily accessible for the researcher.

Participants There were 99 participants in the study, 93 students and six teachers. The student sample

was 84% male (n=78) and 16% female (n=15). Of the six teachers, 67% (n=4) were male and

33% were female (n=2). There were no respondents (n=0) who chose not to indicate their

gender.

36  

Instrumentation Two instruments were used during this study. The VIEW: An Assessment of Problem

Solving Style TM instrument served as an indicator for the three constructs of problem solving

style, orientation to change, manner of processing, and ways of deciding for the students and

teachers in the Virginia Polytechnic Institute and State University Agricultural Technology

program. The second instrument was the Group Embedded Figures Test (GEFT). The GEFT was

used to interpret learning style in terms of field dependence or field independence.

VIEW: An Assessment of Problem Solving Style TM. The participant’s problem solving

style preferences were found using the VIEW: An Assessment of Problem Solving Style TM

instrument. The VIEW: An Assessment of Problem Solving Style TM instrument serves as an

indicator for three independent dimensions of problem solving style. The explorer/developer

continuum specifies a person’s orientation to change. The second continuum, external/internal,

estimates an individual’s preferred means of processing information during problem solving

processes. The ways of deciding, or otherwise known as the people/task continuum, assesses an

individual’s rationale and basis for decisions; towards emotional or logical outcomes.

As of 2007, 16,141 subjects had completed the VIEW: An Assessment of Problem

Solving Style TM assessment (Treffinger, 2008). Treffinger (2008) also reported that 45.1% of the

VIEW: An Assessment of Problem Solving Style TM users are male, 53.9% are female and 1.0%

declined to indicate gender. The mean age for VIEW: An Assessment of Problem Solving

Style TM is 37.7 with a standard deviation of 12.9 and an age range from 11 to 90 years

(Treffinger, 2008). Using Cronbach’s coefficient alpha for the 2007 data, the current sample had

coefficient results of .87 for orientation to change, .86 for manner of processing and .82 for ways

of deciding (Treffinger, 2008). Table 2 describes summary statistic data for data through 2007.

37  

Table 2

2007 VIEW: An Assessment of Problem Solving Style TM Descriptive Statistics (N=16,141)

Statistic

Orientation To

Change

Manner of

Processing

Ways of

Deciding

M 73.9 29.6 35.0

SD 15.9 9.2 8.5

SE 5.7 3.4 3.6

Note: Orientation To Change has a minimum score of 16 a maximum score of 126. Manner of

Processing and Ways of Deciding have a minimum score of 8 and a maximum score of 56. These

statistics were taken from the VIEW: An Assessment of Problem Solving Style TM Technical

Update by Treffinger (2008, p. 1).

The theoretical means for orientation to change, manner of processing, and ways of

deciding are 72, 32, and 32, respectively. Some of the problem solving style constructs are

slightly correlated with each other. Table 3 describes some of the intercorrelations among the

constructs of VIEW: An Assessment of Problem Solving Style TM, Age and Gender from the

international database.

38  

Table 3

Intercorrelations of VIEW Dimensions, Age and Gender (N=10,151)

Variable

Manner of

Processing

Ways of

Deciding Age Gender

Orientation To Change .10** .10** -.11* .14*

Manner of Processing --- .08* .03 .06

Ways of Deciding --- -.04 .31*

Age --- .06

Note: The source of these statistics was taken from the VIEW: An Assessment of Problem Solving

Style Technical Manual, 2nd ed. by Selby et al. (2007, p. 40).

* p < .01. **p < .001.

Group Embedded Figures Test. Learning style was assessed using the Group Embedded

Figures Test (GEFT). The GEFT is recognized as a standard instrument for identifying

individual preferences for field dependence and field independence (Dyer & Osborne, 1996;

Witkin et al., 1971). GEFT has a reliability of .82 for males and .79 for females (Witkin et al.,

1971). Table 4 describes more reliability data about the GEFT instrument.

39  

Table 4

Number Correct: GEFT

Quartiles Men Women

1 0-9 0-8

2 10-12 9-11

3 13-15 12-14

4 16-18 15-18

n 155 242

M 12.0 10.8

SD 4.1 4.2

Note: GEFT has a minimum score of 0 and a maximum score of 18. The source of these statistics

was taken from the Group Embedded Figures Test Manual by Witkin et al. (1971, p. 26).

This instrument was timed. The GEFT has three individual sections. The first section has

seven straightforward items and two minutes were allowed for completion. The participant had

five minutes to complete the second and third sections that have nine items each and are more

complex (Witkin et al., 1971). The number of correctly identified simple shapes in the second

and third sections is the total GEFT score. Scores that were below the national mean are field

dependent while scores above the national mean were field independent (Luk, 1998; Raven et al.,

1993; Witkin et al., 1971).

Procedures Students and teachers received written notification in advance four times prior to the

study. The first contact was a letter was that was delivered during class time for the students and

40  

delivered through campus mail to the faculty. A subsequent and similar electronic notice was

delivered the next week. The third and fourth contacts were emails delivered the week of the

study. After the data were collected, participants received a “Thank you” email. Additional oral

announcements were made during the three weeks prior to the study. An alternative date was

scheduled for students and instructors who were absent during the days when data were

collected. One-hundred percent of the faculty (n=6) responded to the instruments and 96%

(n=93) of the students responded to both instruments.

Data Analysis Data from the VIEW: An Assessment of Problem Solving Style TM instrument were

collected electronically and converted to an Excel file. It was then analyzed in the JMP 7.0 for

Windows TM statistical package. GEFT and academic grade data were entered into JMP 7.0 by

the researcher. A significance level of 5% was set for all research objectives a priori. A 5% level

is considered to be an acceptable level for Type I error (Zar, 1999).

Objective one was accomplished by reporting the following for each dimension of

VIEW: An Assessment of Problem Solving Style TM: mean and standard deviation for first year

and second year students, student degree options, student grade point averages, as well as the

descriptive statistics for the student sample age range, total cumulative student sample and the

total cumulative instructor sample. An analysis of variance for all variables was conducted.

Objective two was accomplished by reporting the following in accordance to the total

GEFT score: mean and standard deviation for first year and second year students, student grade

point averages, student degree options, student sample age range, and descriptive statistics for

41  

the total cumulative student sample and all teacher samples. An analysis of variance for all

variables was conducted.

Objective three was met by using a bivariate correlation to compare each construct of

VIEW: An Assessment of Problem Solving Style TM and GEFT for the following groups: first

year and second year students, student degree options, student grade point averages, student

ages, the total student sample, and the sample of instructors.

Objective four was assessed by conducting a multiple linear regression to produce a

grade explaining equation. The factors included in the regression analysis were student

orientation to change, manner of processing, ways of deciding, and learning style scores and the

teachers’ orientation to change, manner of processing, ways of deciding, and learning style

scores. A backward stepwise regression with a 5% a priori alpha level was used to identify a

statistically sound model.

Summary This chapter highlighted the methods used by the researcher in this study of the cognitive

style of students and teachers at the Virginia Polytechnic Institute and State University

Agricultural Technology program in regards to academic performance. This study was

descriptive, correlational, and explanatory. This chapter also described the study’s variables and

the context in which the study was conducted. Cognitive style was assessed within the context of

the Virginia Polytechnic Institute and State University Agricultural Technology program faculty

and students enrolled in the Personnel Management course. The VIEW: An Assessment of

Problem Solving Style TM and GEFT instruments were used to assess learning and problem

solving styles. Data were collected online and on paper, respectively. Data analysis procedures

42  

and statistical analyses including means, standard deviations, bivariate correlational analyses,

multiple sample analyses of variances, and a multiple linear regression were described in this

chapter.

Chapter 4 will report the results of this study.

43  

Chapter 4

Results

Chapter 4 highlights the results from this study. It is organized in terms of the four

specific research objectives that were identified in Chapter 1. This chapter first reports

differences in problem solving and learning styles for groups students and teachers, student

degree options, academic years, ages, and grade point averages (GPA). Then the relationship

between problem solving styles and learning style was identified. Lastly, Chapter 4 recognizes

the explanatory nature of students’ end of course grades as a result of student and teacher

problem solving and learning styles.

Objective One: Determine if there is a difference in problem solving styles between the Virginia

Polytechnic Institute and State University Agricultural Technology Program students and

teachers, student degree options, academic years, ages, and grade point averages.

Descriptive statistics were first identified within the population. Table 5 describes the

teacher sample problem solving style.

44  

Table 5

Teacher Problem Solving Style Summary Statistics (n=6)

Statistic Orientation To Change Manner of Processing Ways of Deciding

M 81.67 31.17 40.00

SD 14.47 12.58 10.22

SE 5.90 5.13 4.17

Note: Orientation To Change has a minimum score of 16 and a maximum score of 126. Manner

of Processing and Ways of Deciding have a minimum score of 8 and a maximum score of 56.

The instructor manner of processing mean score is close to the national and theoretical

mean of for this domain. Table 6 describes the student sample’s problem solving style in greater

detail.

Table 6

Student Problem Solving Style Summary Statistics (n=93)

Statistic Orientation To Change Manner of Processing Ways of Deciding

M 77.35 32.03 33.72

SD 14.45 9.53 7.63

SE 1.50 0.99 0.79

Note: Orientation To Change has a minimum score of 16 and a maximum score of 126. Manner

of Processing and Ways of Deciding have a minimum score of 8 and a maximum score of 56.

The student manner of processing and ways of deciding means were close to the national

and theoretical mean for these domains. Table 7 outlines the orientation to change scores for

specific groups of the study’s student sample.

45  

Table 7

Orientation to Change Summary Statistics (N=99)

Variable n M SD Range F p

Type 0.50 .48

Student 93 77.35 14.45 40-118

Teacher 6 81.67 14.47 57-93

Student Degree Option 6.07 .02*

Applied Agricultural Management 45 81.07 14.20 41-118

Landscape and Turf Management 48 73.88 13.95 40-100

Academic year 0.05 .82

First Year 50 77.86 13.79 41-118

Second Year 43 76.98 15.35 40-103

Age 0.57 .64

18 13 80.08 18.16 40-108

19 39 79.13 12.44 54-118

20 22 74.82 14.77 41-98

21+ 19 74.79 15.48 47-103

GPA (Quartiles) 2.04 .11

0.00-2.26 24 75.46 15.60 40-118

2.27-2.90 24 72.71 16.12 41-103

2.91-3.45 23 79.52 11.81 59-108

3.46-4.00 22 82.23 12.61 54-103

Note: Orientation to Change has a minimum score of 16 and a maximum score of 126.

* p < .05.

46  

A series of ANOVA tests were conducted between different groups of students and

teachers. A 5% Type I error rate was set a priori. There was a statistically significant difference

between orientation to change scores for students in different degree programs (p = .02).

Landscape and Turf Management students were more likely to be more explorer oriented than

the Applied Agricultural Management students.

Table 8 is outlines the manner of processing scores for different groups of the student

sample.

47  

Table 8

Manner of Processing Summary Statistics (N=99)

Group n M SD Range F p

Type 0.04 .83

Student 93 32.03 9.53 8-54

Instructor 6 31.17 12.58 17-47

Student Degree Option 0.02 .89

Applied Agricultural Management 45 31.89 8.51 13-54

Landscape and Turf Management 48 32.17 10.48 8-51

Academic year 4.32 .04*

First Year 50 30.16 10.11 8-54

Second Year 43 34.86 8.40 13-51

Age 1.02 .39

18 13 35.46 4.24 29-42

19 39 30.13 9.18 12-49

20 22 29.82 11.37 8-51

21+ 19 36.16 9.12 18-47

GPA (Quartiles) 1.22 .31

0.00-2.26 24 30.5 9.66 8-51

2.27-2.90 24 31.04 8.43 13-49

2.91-3.45 23 31.48 8.13 16-49

3.46-4.00 22 35.36 11.52 12-54

Note: Manner of Processing has a minimum score of 8 and a maximum score of 56.

* p < .05.

48  

Manner of Processing scores were reviewed to determine differences between students

and teachers, degree programs, ages, and GPA ranges. The Landscape and Turf Management

student sample did have more variability than the Applied Agricultural Management students in

the manner of processing domain. There was a statistically significant difference between first

and second year students’ manner of processing scores (p = .04). Second year students were

more likely to have internal methods of processing problems. In terms of variability, the 20 year

old student’s manner of processing scores varied the most with a standard deviation of 11.37,

while 18 year old students were tightly arranged with a standard deviation of 4.24.

Table 9 breaks down different student group scores for the ways of deciding problem

solving style continuum.

49  

Table 9

Ways of Deciding Summary Statistics (N=99)

Variable n M SD Range F p

Type 3.67 .06

Student 93 33.72 7.63 15-49

Instructor 6 40.00 10.22 28-53

Student Degree Option 0.10 .75

Applied Agricultural Management 45 33.98 7.06 15-49

Landscape and Turf Management 48 33.48 8.19 17-49

Academic year 1.80 .18

First Year 50 32.74 8.08 15-48

Second Year 43 34.86 6.98 21-49

Age 2.03 .11

18 13 35.46 8.59 21-49

19 39 32.87 8.26 15-49

20 22 33.50 6.38 19-44

21+ 19 34.53 7.21 18-47

GPA (Quartiles) 0.54 .66

0.00-2.26 24 35.40 7.33 18-46

2.27-2.90 24 33.17 6.09 19-49

2.91-3.45 23 32.87 7.77 19-48

3.46-4.00 22 33.36 9.35 15-49

Note: Ways of Deciding has a minimum score of 8 and a maximum score of 56.

50  

The last component of problem solving style, ways of deciding, was also compared

among students and teachers, degree programs, ages, and GPA ranges. There were no significant

differences between students and teachers, degree programs, ages, and GPA ranges for the ways

of deciding domain of problem solving style. Although it was not a statistically significant

relationship, teachers were more task oriented than the student sample.

Objective Two: Determine if there is a difference in learning styles between the Virginia

Polytechnic Institute and State University Agricultural Technology Program students and

teachers, student degree options, academic years, ages, and grade point averages.

Learning style was indicated using the Group Embedded Figures Test (GEFT)

instrument. Scores could range from a minimum score of 0 to a maximum score of 18. Table 10

identifies learning style summary statistics for the study’s population.

51  

Table 10

Learning Style Summary Statistics (N=99)

Variable n M SD Range F p

Type 2.80 .10

Student 93 10.57 4.66 1-18

Instructor 6 13.83 3.92 8-18

Student Degree Option 3.89 .05

Applied Agricultural Management 45 9.60 4.57 1-18

Landscape and Turf Management 48 11.48 4.61 2-18

Academic year 0.92 .34

First Year 50 10.14 5.01 2-18

Second Year 43 11.07 4.22 1-18

Age 1.38 .25

18 13 9.31 4.52 3-17

19 39 10.72 4.65 3-18

20 22 10.64 4.77 1-18

21+ 19 11.05 4.89 2-17

GPA (Quartiles) 0.95 .42

0.00-2.26 24 9.58 4.65 1-17

2.27-2.90 24 10.29 4.80 3-17

2.91-3.45 23 10.65 4.37 2-18

3.46-4.00 22 11.86 4.83 3-18

Note: The GEFT has a minimum score of 0 and a maximum score of 18.

52  

An ANOVA test was conducted between the different groups. A 5% Type I error rate

was predetermined for levels of statistical significance. There were no statistically significant

differences between the learning styles of students and teachers, degree programs, ages, and

GPA ranges.

Objective Three: Determine if there is a relationship between problem solving style and learning

style.

The third objective was to determine if there was a relationship between problem solving

styles and learning styles. The next table identifies correlations between each construct of

problem solving and learning style. Pairwise comparisons were calculated to determine the level

of statistical significance for each correlation. Table 11 outlines the correlations between

problem solving and learning styles for students and teachers.

53  

Table 11

Students and Teachers Problem Solving and Learning Styles Intercorrelations

Variable

Orientation to

Change

Manner of

Processing

Ways of

Deciding

Learning

Style

Students (n = 93)

Orientation To Change --- .17 .11 -.17

Manner of Processing --- .36* .08

Ways of Deciding --- -.07

Learning Style ---

Teachers (n = 6)

Orientation To Change --- -.15 .15 -.48

Manner of Processing --- .34 .56

Ways of Deciding --- .48

Learning Style ---

* p < .05.

In the student sample, there was a statistically significant correlation between the manner

of processing and ways of deciding continuums (p = <.01). Students who were task oriented

decision makers were also likely to be internal processers of information. There were no other

statistically significant correlations between students and teachers. Table 12 describes the

problem solving style and learning style correlation matrix for Agricultural Technology degree

programs.

54  

Table 12

Degree Program Problem Solving and Learning Styles Intercorrelations

Variable

Orientation to

Change

Manner of

Processing

Ways of

Deciding

Learning

Style

Applied Agricultural Management (n = 45)

Orientation To Change --- .28 .29 -.17

Manner of Processing --- .34* -.18

Ways of Deciding --- -.07

Learning Style ---

Landscape and Turf Management (n = 48)

Orientation To Change --- .10 -.05 -.09

Manner of Processing --- .36* .28

Ways of Deciding --- -.05

Learning Style ---

* p < .05.

In the Applied Agricultural Management degree program, there was a statistically

significant correlation between the manner of processing and ways of deciding continuums (p =

.02 ). In the Landscape and Turf Management degree program, there was a statistically

significant correlation between the manner of processing and ways of deciding continuums (p =

.01). Students in both degree programs who were task oriented decision makers were also likely

to be internal processers of information. There were no other statistically significant correlations

55  

between students and teachers. Table 13 describes the problem solving style and learning style

correlation matrix for first and second year students.

Table 13

Academic Year Problem Solving and Learning Styles Intercorrelations

Variable

Orientation to

Change

Manner of

Processing

Ways of

Deciding

Learning

Style

First Year Students (n = 50)

Orientation To Change --- .24 .10 .02

Manner of Processing --- .35* .04

Ways of Deciding --- -.18

Learning Style ---

Second Year Students (n = 43)

Orientation To Change --- .10 .14 -.41*

Manner of Processing --- .32* 0.10

Ways of Deciding --- .07

Learning Style ---

* p < .05.

Manner of processing and ways of deciding were significantly correlated for first year

students (p = .01). For second year students, there was a statistically significant correlation

between the manner of processing and ways of deciding continuums (p = .04). There was also a

statistically significant correlation between orientation to change and learning style (p = <.01).

Both first and second year students who were task oriented decision makers were likely to also

56  

be internal processers of information. There were no other statistically significant correlations

between students and teachers. Table 14 describes the problem solving style and learning style

correlation matrix for the different age groups in the study’s sample.

57  

Table 14

Problem Solving and Learning Styles Intercorrelations Between Age Groups

Variable

Orientation to

Change

Manner of

Processing

Ways of

Deciding

Learning

Style

18 Years (n = 13)

Orientation To Change --- -.18 -.71* -.28

Manner of Processing --- .62* .07

Ways of Deciding --- .02

Learning Style ---

19 Years (n = 39)

Orientation To Change --- .23 .38* -.09

Manner of Processing --- .31 -.01

Ways of Deciding --- -.26

Learning Style ---

20 Years (n = 22)

Orientation To Change --- -.01 .01 -.38

Manner of Processing --- .39 .08

Ways of Deciding --- .09

Learning Style ---

21 Years and Older (n = 19)

Orientation To Change --- .62* .29 -.06

Manner of Processing --- .19 .23

Ways of Deciding --- -.15

Learning Style ---

* p < .05.

58  

In the 18 year old students, there was a statistically significant correlation between the

manner of processing and ways of deciding continuums (p = .02). Eighteen year old students who

were task oriented decision makers were also likely to be internal processers of information.

There was also a statistically significant correlation between orientation to change and ways of

deciding (p = <.01). Students who were 18 years old who are task oriented decision makers are

also likely to be explorer oriented in their orientation to change. In the group of students who

were 19 years old, there was a statistically significant correlation between orientation to change

and ways of deciding (p = .02). Nineteen year old students who were task oriented decision

makers were more likely to be developer oriented in their orientation to change. There was a

statistically significant correlation between the manner of processing and orientation to change

continuums for students that were at least 21 years of age (p = <.01). Students in this age range

who were internal processers of information were likely to be developer oriented in their

orientation to change. Table 15 describes the problem solving style and learning style correlation

matrix for students with similar GPAs.

59  

Table 15

Problem Solving and Learning Styles Intercorrelations Between GPA Groups

Variable

Orientation to

Change

Manner of

Processing

Ways of

Deciding

Learning

Style

GPA 0.00-2.26 (n = 24)

Orientation To Change --- .29 .26 -.27

Manner of Processing --- .03 .33

Ways of Deciding --- .16

Learning Style ---

GPA 2.27-2.90 (n = 24)

Orientation To Change --- .38 .36 -.10

Manner of Processing --- .06 .12

Ways of Deciding --- .05

Learning Style ---

GPA 2.91-3.45 (n = 23)

Orientation To Change --- -.03 .13 -.11

Manner of Processing --- .47* -.42

Ways of Deciding --- -.37

Learning Style ---

GPA 3.46-4.00 (n = 22)

Orientation To Change --- -.18 -.23 -.42

Manner of Processing --- .74* .05

Ways of Deciding --- -.06

Learning Style ---

* p < .05.

60  

In the third quartile of GPAs, 2.91-3.45, there was a statistically significant correlation

between the manner of processing and ways of deciding continuums (p = .02). There was also a

statistically significant correlation between manner of processing and ways of deciding in the

upper GPA quartile, 3.46-4.00 (p = <.01). Students in the upper half of the GPA range who were

internal processers of information were also likely to be task oriented decision makers.

Objective Four: Explain students’ end of course grades using problem solving and learning

styles.

In order to determine if student and teacher problem solving and learning styles could

explain end of course grades, a multiple linear regression was conducted on grade data from all

Agricultural Technology fall courses from 2006 and 2007. Data from students that took both

instruments were used in the regression. A backward stepwise regression with a 5% a priori

alpha level was used to determine a model that explained grades based on student and teacher

cognitive styles. Each domain in the VIEW: An Assessment of Problem Solving Style TM

(orientation to change, manner of processing, and ways of deciding) and GEFT (field

dependence/ independence) were used as preliminary variables in the model. Table 16 describes

the regression analysis.

61  

Table 16

Regression Analysis to Explain End of Course Grades (N = 440)

Variable β SE t p

Intercept 84.24 4.31 19.56 <.01*

Student Orientation to Change 0.12 0.04 3.02 <.01*

Student Manner of Processing 0.14 0.06 2.26 0.02

Teacher Manner of Processing -0.19 0.05 -3.72 <.01*

Teacher Ways of Deciding -0.30 0.06 -4.73 <.01*

* p <.05

A regression analysis showed that a combination of student orientation to change, student

manner of processing, teacher manner of processing, and teacher ways of deciding scores

significantly explained student’s end of course grades, F(14.54) and significance of p = <.0001.

R2 for this model was .12 and adjusted R2 was .11. This model explained 11% of the variance

(adjusted) in final course grades for all fall courses taught by the six faculty members in the

Agricultural Technology program.

Summary

This chapter outlined the results according to each objective of the study. The results

were organized by objective. The objectives of the study were:

1. Determine if there is a difference in problem solving styles between the Virginia Polytechnic Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades;

2. Determine if there is a difference in learning styles between the Virginia Polytechnic

Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades;

3. Determine if there is a relationship between problem solving style and learning style; and,

62  

4. Explain students’ end of course grades using problem solving and learning styles.

Chapter 5 will provide conclusions and recommendations based on the findings.

63  

Chapter 5

Discussion of Findings, Conclusions and Implications

This chapter contains a summary and discussion of the findings from this study. Study

conclusions and recommendations for practice and further research were also included.

Human behavior is difficult to assess because it is largely a function of many distinctly

different facets; including the environment, cognitive resource, cognitive affect, and cognitive

effect (Kirton, 2003). The central focus of this study was to examine and identify student and

teacher cognitive style and its relation to students’ end of course grades in the Virginia

Polytechnic Institute and State University Agricultural Technology program. It is important to

remember that the results of this study cannot be generalized beyond the specific sample. The

results and implications contained herein are framed within the context of the study’s sample.

The specific research objectives of this study were:

1. Determine if there was a difference in problem solving styles between the Virginia Polytechnic Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades;

2. Determine if there was a difference in learning styles between the Virginia Polytechnic

Institute and State University Agricultural Technology Program students and teachers, student degree options, academic years, ages, and academic course grades;

3. Determine if there was a relationship between problem solving style and learning style;

and,

4. Explain students’ end of course grades using problem solving and learning styles.

Discussion of the Findings

Objective One: Determine if there was a difference in problem solving styles between the

Virginia Polytechnic Institute and State University Agricultural Technology Program students

64  

and teachers, student degree options, academic years, ages, and academic course grades.

Problem solving style is broken down into three domains: orientation to change, manner of

processing, and ways of deciding. The discussion for this objective is organized by each domain

of problem solving style. The landscape and turf management students were more explorer

oriented in the orientation to change domain, as compared to the applied agricultural

management students. Students in the two different degree programs will be entering into very

distinct and different career fields. Selby et al. (2007) have found distinct differences between

other broader occupational careers as well.

There were relatively few distinctions between group classifications among the manner

of processing continuum. Second year students were more likely to have more internal methods

of processing information as compared to their first year counterparts. It is important to

acknowledge that problem solving style remains relatively steadfast and resistant to change

(Treffinger, Isaksen, & Stead-Dorval, 2005). Therefore, the distinction between these groups of

students is only a factor of the students who self selected into the specified entry year of the

program. However, the distinction between first and second year students’ manner of processing

scores is important to notice for maintaining quality teaching practices. It serves as a quantitative

reminder that each class may be different than the last. Practically speaking, younger individuals

tended to have more external methods of processing information. Again, problem solving style

does not typically change a great deal over time (Treffinger et al., 2005). This difference is likely

a result of the students who happened to elect into the program at a younger age. In terms of

practical significance, students with a GPA in the 3.46-4.00 range were different from students

with a GPA between the ranges of 0.00-3.45. Students with a high GPA were more likely to be

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classified as strong internal processors. All other groups of students and teachers were not

statistically different.

There were a few important traits to bring attention to in the ways of deciding domain of

problem solving style. Agricultural Technology program teachers were likely to be more task-

oriented problem solvers than all students enrolled in the program. The student sample was

largely more people oriented than the teacher sample. It should be noted that second year

students tended to have slightly more task oriented outlooks. This is yet another distinction

between different class types. Instructors must continue to acknowledge differences between

their style preferences as well as differences within their students.

Objective Two: Determine if there is a difference in learning styles between the Virginia

Polytechnic Institute and State University Agricultural Technology Program students and

teachers, student degree options, academic years, ages, and academic course grades. The mean

GEFT score for Agricultural Technology program students was 10.57, indicating field dependent

students. However, in a study by Rudd, Baker and Hoover (2000) the mean GEFT score for

students in a four year degree program was 12.88, indicating field independent students. This

difference is another important distinction between the types of students that may be attracted to

one type of program over another. There were two important learning style distinctions within

the population. Agricultural Technology program teachers were more field independent than the

student body. It was also apparent that there were slightly higher levels of field independence as

students got older. These findings align with the findings from Witkin et al (1971), in that after

24 years of age, individuals will tend develop more field independent tendencies. Students

enrolled in the landscape and turf management degree program were also more likely to be more

field independent than students in the applied agricultural management degree option. Cano

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(1999) suggested that freshmen entering into horticulture, agronomy, animal sciences and

management degree programs were field independent. Students in the upper quartile of the GPA

distribution also had the highest levels of field independence. This finding concurs with a study

by Luk (1998) in that field independence was associated with higher levels of academic

achievement.

Objective Three: Determine if there is a relationship between problem solving style and

learning style. Agricultural Technology students’ ways of deciding scores were significantly

correlated with their manner of processing scores. Task oriented problem solvers were very

likely to also be internal processors. In comparison to the VIEW: An Assessment of Problem

Solving Style TM database, this correlation is much higher than the .08 correlation cited by Selby

et al. (2007). Other problem solving style intercorrelations did not differ largely from the VIEW:

An Assessment of Problem Solving Style TM database.

There were small correlations between learning style and problem solving style. However

there was only one correlation that was statistically different from zero. Therefore, it is

reasonable to conclude that there is only a weak correlation between GEFT and VIEW: An

Assessment of Problem Solving Style TM. In other words, field dependence and field

independence are not directly tied to the problem solving style constructs orientation to change,

manner of processing and ways of deciding in this specific population.

Early work in the three problem solving style constructs was based in research by

learning style theorists such as Dunn and Dunn. The researcher found no literature supporting a

link between work by Dunn and Dunn and Witkin. The GEFT learning style instrument has

traditionally been regarded as an analytical and perceptual instrument, whereas Dunn and Dunn’s

work encompasses broader spectrums of learning style. A review of the literature pertaining to

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problem solving style describes learning style as a component of nearly all three dimensions of

problem solving style. This may make it difficult for any one problem solving style domain to

have a significant correlation with the GEFT’s version of learning style.

Objective Four: Explain students’ end of course grades using differences in student and

teacher problem solving and learning styles. An equation was developed that explained 11% of

the variance in students’ end of course grades. There were four important factors in the equation;

student orientation to change, student manner of processing, teacher manner of processing, and

teacher ways of deciding. Style is only one component of cognitive effect, which in turn is only a

one factor of an individual’s cognitive function. Identifying components that explain behavioral

outcomes is essential in explaining, evaluating, and studying human behaviors.

Conclusions

After an analysis of the results and findings from this study, there are several central

themes that have emerged. These conclusions are:

• Some individuals may be more likely to have certain cognitive effect style dispositions relative to their degree program and year in school;

• The VIEW: An Assessment of Problem Solving Style TM and Group Embedded

Figures Test are not highly related in this population.

• Both student and teacher problem solving style contribute to explaining students’ end of course grades.

Recommendations

As a result of this study, several recommendations have been developed that will impact

educational institutions, teaching practices and shape further research in this area. These

recommendations are organized by recommendations for practice and for further research.

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Recommendations for practice. One recommendation of this study is that educators be

made aware of the stylistic differences in their classroom and the impact it may have on their

students’ academic success. Effective teachers need to differentiate their instruction techniques

in order to best accommodate the variety of learners in their classroom. As evidenced in this

study, class style composition may be extremely diverse. A well versed educator will anticipate

these differences in their classroom and design instruction that strengthens style awareness and

prompts all learners to interpret and evaluate the implications of their own preferences as well as

those of others. There are definite relationships between students’ end of course grades and the

style preferences of students and teachers. It must be recognized that cognitive styles do

contribute to how individuals and groups approach novel situations and how we think creatively,

but they do not completely control human behavior. This reiterates work by Selby et al. (2007),

in that problem solving styles are not fixed, expectations for behavior, or excuses for exceptional

performance.

Recommendations for research. Longitudinal research tracking student academic

performance and style scores would enhance the knowledge in this field. The sample of students

enrolled in the spring 2008 semester are not representative of all agricultural students or students

enrolled in agricultural associate’s degree programs. A study that examines the style preferences

of other agricultural associate’s degree program students and teachers would be beneficial. In

this study, the class enrollments were random. More research should be done that tracks student

performance in different courses in order to fully identify the effect of cognitive style on

individual academic performance. Research that takes years of teaching experience into account

in combination with styles to explain grades may find interesting results. This study could easily

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be expanded to determine the effects and interactions between student and teacher style and

course content areas.

The VIEW: An Assessment of Problem Solving Style TM instrument is grounded in Dunn

and Dunn’s learning style work. More research is necessary that examines and explains VIEW:

An Assessment of Problem Solving Style TM using the Group Embedded Figures Test (GEFT) as

well as other learning style instruments. This experimental design could be easily expanded to

include critical thinking, decision making, personality, and affective measures of human

function.

End of course grades are only one measure of a student’s success. Qualitative research

that assesses an individual’s perceptions of courses, program administration, and teachers would

be beneficial in explaining both problem solving style and end of course grades. This may lead to

further work examining and identifying coping behaviors and their relationship with cognitive

function, perception, and performance.

As a result of this study, stylistic differences were identified in college degree fields. It

would be both pedagogically important as well as valuable to identify other degree programs in

agriculture that may attract individuals with specific style preferences. Further research should

be done to explain why and how individuals with specific style preferences are successful in

specific career fields.

For the most part, the population in this study was not different from the national

population’s problem solving or learning styles. More research should be done to investigate the

role that parents have on their children’s stylistic preferences. Understanding the role that peers,

authority figures, role models, caregivers and teachers have on the style preference development

of youth will have a significant impact on educational trends, career placement and workforce

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development in the future. Individuals should be closely monitored during adolescent, early

adult, and adult time periods to determine if, why, and how key events, individuals or

experiences shift style preferences.

Group dynamics play a key role in workplace, career and community success. Problem

solving and learning style awareness and appreciation activities may be incorporated into youth

leadership organizations for agriculture, such as the FFA or 4-H. The role individuals have in

groups, in comparison to their style preferences, should be researched further. It would also be

interesting to determine how, when, and why students move into coping behaviors due to

stylistic differences and the impact it has on their academic achievement.

Summary

An individual’s preferences for solving problems and learning are evidenced in routine

mannerisms and unique personal behavior identifiers. Acknowledging an individual’s different

stylistic outlooks is a primary step for educators in ensuring all students an equal chance to

quality education. There are and will be distinct differences in problem solving and learning

styles that impact our daily lives. Cognitive preferences have profound implications on behavior.

In order to best meet individual needs, these personal style traits must be acknowledged,

addressed, and accepted by our peers, teachers, and the world.

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Witkin, H. A., Oltman, P. K., Raskin, E., & Karp, S. A. (1971). Group Embedded Figures Test Manual. Palo Alto, CA: : Consulting Psychologist Press, Inc.

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Appendix A

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From: Sanders, Helen (CLPubSrvUK) Sent: 11 March 2008 16:17 To: 'Permissions Mailbox' Subject: RE: Reprint Figure from Kirton 2003 Dear Ed McCann, Re: Adaption-Innovation, In The Context of Diversity and Change” by Michael Kirton’ Further to your recent emails permission is granted for use of the above material in your forthcoming Thesis, subject to the following conditions: 1. The material to be quoted/produced was published without credit to another source. If another source is

acknowledged, please apply directly to that source for permission clearance. 2. Permission is for non-exclusive, English language rights, and covers use in your Thesis only. Any further use

(including storage, transmission or reproduction by electronic means) shall be the subject of a separate application for permission.

3. Full acknowledgement must be given to the original source, with full details of figure/page numbers, title,

author(s), publisher and year of publication. Yours sincerely, Helen Sanders Taylor and Francis Books UK Please Note: My email address has changed to helen.sanders@contractor.cengage.com Helen Sanders Permissions & Subsidiary Rights Administrator Taylor and Francis Books UK Tel: + 44 (0) 1264 343090 Fax + 44 (0) 1264 342792 Cengage Learning Services Limited, Cheriton House, North Way, Andover, Hants, SP10 5BE, a limited company registered in England and wales under company number 929655. This e-mail is confidential and may be privileged. Any opinions expressed in this communication are not necessarily those of the company. It may be read, copied and used only by the intended recipient. If you have received it in error please contact us immediately by return email. Please then delete the email and do not disclose the contents to any person. Although it is believed, but not warranted, that this email and any attachments are virus-free, it is your responsibility to check this.

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Appendix B

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March 31, 2008

Dr. Don Treffinger Center for Creative Learning, Inc. 4921 Ringwood Meadow Sarasota, FL 34235

Dear Don:

I am now drawing near to the completion of my master’s research work. As you may already be aware, I am using VIEW and the Group Embedded Figures Test (GEFT) to predict student’s grades. As I approach my final preparation phases, I would like your permission to reprint in my thesis excerpts from the following:

Treffinger, Selby, E. C., Isaksen, S. G., & Crumel, J. H. (2007). An Introduction to Problem-Solving Style. Sarasota, FL: Center for Creative Learning.

The excerpts to be reproduced are:

• The VIEW theoretical framework that was published on page 3; and • The table that is also on page 3 of An Introduction to Problem Solving Style (2007). The

table describes what problem solving is and is not in clear detail.

The requested permission extends to any future revisions and editions of my dissertation, including non-exclusive world rights in all languages, and to the prospective publication of my dissertation by UMI Company. These rights will in no way restrict republication of the material in any other form by you or by others authorized by you. Your signing of this letter will also confirm that you own [or your company owns] the copyright to the above-described material.

If these arrangements meet with your approval, please sign this letter where indicated below and return it to me in the enclosed return envelope. Thank you very much.

If you have any questions, please feel free to call or email me.

Sincerely,

Ed McCann Virginia Tech AEE

PERMISSION GRANTED FOR THE USE REQUESTED ABOVE:

_______________________________________ Date: _________________ Dr. Donald J. Treffinger

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Appendix C

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82  

Appendix D

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84  

85  

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Appendix E

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Virginia Polytechnic Institute and State University

Informed Consent for Participants in Research Projects Involving Human Subjects

Title of Project: Cognitive Effect Indicators: The Impact of Student and Teacher Styles On Course Grades

Investigators: Mr. Edward W. McCann, Jr., Master’s Degree Student, Virginia Tech Dr. Thomas W. Broyles, Assistant Professor, Virginia Tech

I. Purpose of this research

This research will help explain the many different personality preferences and traits that contribute to course grades. Specifically, the purpose of this study is to closely examine student and instructor personal problem solving and learning style preferences. This new knowledge will help to promote better teaching practices in the greater educational community. Participants will be the 94 students and 6 teachers in the Virginia Polytechnic Institute and State University Agricultural Technology Program.

II. Procedures

You will be invited to complete an online survey and a paper based exercise using VIEW: An Assessment of Problem Solving Style TM and the Group Embedded Figures Test (GEFT). Each of these instruments only requires 5-10 minutes to complete. Students will participate during the Personnel Management Course (AT 0224) and the instructors will participate during a bi-weekly faculty meeting. The students’ grade data will be secured via an online database that the Agricultural Technology program uses. At no time will your individual information or scores be released to anyone other than the researchers involved in the project without your written consent.

III. Risks

This study has been reviewed and approved by the Virginia Tech Institutional Review Board. It received the “Exempt” status which means that it is seen as the safest of all possible research. Individual answers and identities of the participants will be protected at all times.

(Continued)

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IV. Benefits

This research will help uncover how problem solving style and learning style may impact students’ academic end of course grades. It will also help to promote better teaching practices in the educational community. By participating in this study you will receive feedback from both the VIEW: An Assessment of Problem Solving Style TM and the Group Embedded Figures Test (GEFT). These instruments are indicators of your preferences for problem solving style and learning style. They are NOT indicative of your abilities. They help to depict your preferences for learning and interacting with others. No promise or guarantee of benefits have been made to encourage your participation.

V. Extent of Anonymity and Confidentiality

Protecting your identity is a top priority of this study. By participating in this research project, your information will be kept strictly confidential. At no time will information be released that allows an individual to be identified. At no time will the researchers release the results of the study to anyone other than individuals working on the project, without your written consent. Only the research team, Mr. Ed McCann, Jr. and Dr. Tom Broyles will have access to your data.

It is possible that the Institutional Review Board (IRB) may view this study’s collected data for auditing purposes. The IRB is responsible for the oversight of the protection of human subjects involved in research.

VI. Compensation

There is no compensation for participating in this study.

VII. Freedom to withdraw

Participants are free to withdraw from the study at any time without penalty. Subjects are free to not answer any questions without penalty.

VIII. Subject’s responsibilities

I voluntarily agree to participate in this study. I have the following responsibilities:

• Answer each question on the VIEW: An Assessment of Problem Solving Style TM and the Group Embedded Figures Test (GEFT) instruments as honestly as possible per the researcher’s instructions.

(Continued)

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IX. Subject’s permission

I have read and understand the Informed Consent and conditions of this project. I have had all of my questions answered. I hereby acknowledge the above and give my voluntary consent:_____ Yes _____No.

___________________________________________ Date___________ Signature

Should I have pertinent questions about this research, I may contact:

Dr. Thomas Broyles Mr. Edward McCann Assistant Professor Master’s Degree Student HUtbroyles@vt.eduUH HUemccann1@vt.eduU (540) 231-8188 (540) 231-0994 Mr. David M. Moore IRB Chairperson HUmoored@vt.eduUH (540) 231-4991

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Appendix F

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Virginia Polytechnic Institute and State University

Release of Information for Participants in Research Projects Involving Human Subjects

Title of Project: Cognitive Effect Indicators: The Impact of Student and Teacher Styles On Course Grades

Investigators: Mr. Ed W. McCann, Jr., Master’s Degree Student, Virginia Tech Dr. Thomas W. Broyles, Assistant Professor, Virginia Tech

I ________________________________ do hereby give permission to Mr. Ed McCann, Print name

Jr. and Dr. Thomas W. Broyles to review my educational records and use them for this research project. I acknowledge that my records are completely confidential and will not be shared with anyone other than the research team without my written consent.

I realize that the purpose of this study is to closely examine student and instructor personal problem solving and learning style preferences. It will help will help explain the many different personality preferences and traits that contribute to course grades. The knowledge gained will help:

• Understand student and teacher relationships, • Develop better teaching methods, and • Improve teaching strategies in the Agricultural Technology program.

The participants will be the 94 students and 6 teachers in the Virginia Tech Agricultural Technology Program.

Signed Date

Should I have pertinent questions about this research, I may contact:

Dr. Thomas Broyles Mr. Ed McCann Assistant Professor Master’s Degree Student HUtbroyles@vt.eduUH HUemccann1@vt.eduU (540) 231-8188 (540) 231-0994

Mr. David M. Moore IRB Chairperson HUmoored@vt.eduUH (540) 231-4991

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Appendix G

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LETTER 1 – HANDED OUT DURING CLASS (STUDENTS)/CAMPUS MAIL (FACULTY) Mr. Ed McCann, Jr. Dr. Tom Broyles Master’s Degree Candidate Assistant Professor, Virginia Tech 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 Dear Participant: We are a research team in the Agricultural and Extension Education Department here at Virginia Tech. Mr. Ed McCann is currently working to fulfill his research requirement for his master’s degree programs. This research will help explain the many different personality preferences and traits that contribute to course grades. Specifically, the purpose of this study is to closely examine student and instructor personal problem solving and learning style preferences. This new knowledge will help to promote better teaching practices in the greater educational community.

This research will help uncover how problem solving style and learning style may impact students’ academic end of course grades. The knowledge gained will help:

• Understand student and teacher interactions, • Develop better teaching methods, and • Improve teaching strategies in the Agricultural Technology program.

Faculty and students in the Virginia Tech Agricultural Technology program are eligible to

participate. Your participation in this study is voluntary. This study will take place in late February. There are several benefits that you will receive by participating in this study. By participating in

this study you will receive free feedback from both the VIEW: An Assessment of Problem Solving Style TM and the Group Embedded Figures Test (GEFT). These instruments are indicators of your problem solving style and learning style. They are NOT indicative of your abilities but help to depict your preferences for learning and interacting with others. Should you have any questions about this study, please feel free to contact us. Our contact information is: 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 Sincerely, Ed McCann, Jr. Tom Broyles

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EMAIL 2 From: Mr. Ed McCann, Dr. Tom Broyles Subject: Research Participants Needed Mr. Ed McCann Dr. Tom Broyles Master’s Degree Candidate Assistant Professor 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 Dear Participant:

We are a research team in the Agricultural and Extension Education Department here at Virginia Tech. Mr. Ed McCann is currently working to fulfill his research requirement for his master’s degree programs. This research will help explain the many different personality preferences and traits that contribute to course grades. Specifically, the purpose of this study is to closely examine student and instructor personal problem solving and learning style preferences. This new knowledge will help to promote better teaching practices in the greater educational community.

This research will help uncover how problem solving style and learning style may impact students’ academic end of course grades. The knowledge gained will help:

• Understand student and teacher interactions, • Develop better teaching methods, and • Improve teaching strategies in the Agricultural Technology program.

Faculty and students in the Virginia Tech Agricultural Technology program are eligible to

participate. Your participation in this study is voluntary. This study will take place in late February. There are several benefits that you will receive by participating in this study. By participating in

this study you will receive free feedback from both the VIEW: An Assessment of Problem Solving Style TM and the Group Embedded Figures Test (GEFT). These instruments are indicators of your problem solving style and learning style. They are NOT indicative of your abilities but help to depict your preferences for learning and interacting with others. Should you have any questions about this study, please feel free to contact us. Our contact information is: Ed McCann Tom Broyles 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 Sincerely, Ed McCann, Jr. Tom Broyles Virginia Tech AEE Virginia Tech AEE

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EMAIL 3 AND 4: WEEK OF STUDY - STUDENTS From: Mr. Ed McCann, Jr., Dr. Tom Broyles Subject: Research Reminder! Mr. Ed McCann, Jr. Dr. Tom Broyles Master’s Degree Candidate Assistant Professor, Virginia Tech 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 Dear Participant:

During this week, a research project will be carried out in AT 0224: Personnel Management. This research will help explain the many different personality preferences and traits that contribute to course grades. Specifically, the purpose of this study is to closely examine student and instructor personal problem solving and learning style preferences. This new knowledge will help to promote better teaching practices in the greater educational community.

This research will help uncover how problem solving style and learning style may impact students’ academic end of course grades. The knowledge gained will help:

• Understand student and teacher interactions, • Develop better teaching methods, and • Improve teaching strategies in the Agricultural Technology program.

Faculty and students in the Virginia Tech Agricultural Technology program are eligible to

participate. Your participation in this study is voluntary. This study will take place this week in Personnel Management Class! Please be sure to bring your computer.

Faculty and students in the Virginia Tech Agricultural Technology program are eligible to

participate. Your participation in this study is voluntary. This study will take place this week! If you would like to participate, please attend your regularly scheduled class meeting for more information.

There are several benefits that you will receive by participating in this study. By participating in

this study you will receive free feedback from both the VIEW: An Assessment of Problem Solving Style TM and the Group Embedded Figures Test (GEFT). These instruments are indicators of your problem solving style and learning style. They are NOT indicative of your abilities but help to depict your preferences for learning and interacting with others. 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 I look forward to meeting you during the Personnel Management course this week. Sincerely, Ed McCann, Jr. Tom Broyles Virginia Tech AEE Virginia Tech AEE

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EMAIL 3 AND 4: WEEK OF STUDY - FACULTY From: Mr. Ed McCann, Jr., Dr. Tom Broyles Subject: Research Reminder! Mr. Ed McCann, Jr. Dr. Tom Broyles Master’s Degree Candidate Assistant Professor, Virginia Tech 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 Dear Participant:

During this week, a research project will be carried out in the faculty meeting. This research will help explain the many different personality preferences and traits that contribute to students’ course grades. Specifically, the purpose of this study is to closely examine student and instructor personal problem solving and learning style preferences. This new knowledge will help to promote better teaching practices in the greater educational community.

This research will help uncover how problem solving style and learning style may impact students’ academic end of course grades. The knowledge gained will help:

• Understand student and teacher interactions, • Develop better teaching methods, and • Improve teaching strategies in the Agricultural Technology program.

Faculty and students in the Virginia Tech Agricultural Technology program are eligible to

participate. Your participation in this study is voluntary. This study will take place this week in the faculty meeting! Please be sure to bring your computer.

Faculty and students in the Virginia Tech Agricultural Technology program are eligible to

participate. Your participation in this study is voluntary. This study will take place this week! If you would like to participate, please attend your regularly scheduled class meeting for more information.

There are several benefits that you will receive by participating in this study. By participating in

this study you will receive free feedback from both the VIEW: An Assessment of Problem Solving Style TM and the Group Embedded Figures Test (GEFT). These instruments are indicators of your problem solving style and learning style. They are NOT indicative of your abilities but help to depict your preferences for learning and interacting with others. 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 I look forward to seeing you at the faculty meeting this week. Sincerely, Ed McCann, Jr. Tom Broyles Virginia Tech AEE Virginia Tech AEE

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EMAIL THANK YOU FOLLOW UP From: Mr. Ed McCann, Dr. Tom Broyles Subject: Thank You! Mr. Ed McCann Dr. Tom Broyles Master’s Degree Candidate Assistant Professor 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 Dear Participant:

I want to thank you for participating in the recent research project regarding problem solving and learning style. This research will help explain the many different personality preferences and traits that contribute to course grades. Specifically, the purpose of this study is to closely examine student and instructor personal problem solving and learning style preferences. This new knowledge will help to promote better teaching practices in the greater educational community.

This research will help uncover how problem solving style and learning style may impact students’ academic end of course grades. The knowledge gained will help:

• Understand student and teacher interactions, • Develop better teaching methods, and • Improve teaching strategies in the Agricultural Technology program.

Faculty and students in the Virginia Tech Agricultural Technology program were eligible to

participate. Your participation in this study was voluntary. You participated in this study in late February. If you would like to discuss the results from the two assessments, please contact us.

I hope that you found the free feedback from both the VIEW: An Assessment of Problem Solving

StyleTM and the Group Embedded Figures Test (GEFT) beneficial. These instruments are indicators of your problem solving style and learning style. They are NOT indicative of your abilities but help to depict your preferences for learning and interacting with others. Should you have any questions about this study, please feel free to contact us. Our contact information is: 1320 Litton Reaves Hall 268 Litton Reaves Hall Blacksburg, VA 24060 Blacksburg, VA 24060 ed.mccann@vt.edu tbroyles@vt.edu (540) 231-0994 (540) 231-8188 Sincerely, Ed McCann, Jr. Tom Broyles Virginia Tech AEE Virginia Tech AEE

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Appendix H

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Appendix I

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